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An Optimal Task Scheduling Algorithm in Wireless Sensor Networks
Abstract
Sensing tasks should be allocated and processed among sensor nodes in minimum times so that users can draw useful conclusions through analyzing sensed data. Furthermore, finishing sensing task faster will benefit energy saving, which is critical in system design of wireless sensor networks. To minimize the execution time (makespan) of a given task, an optimal task scheduling algorithm (OTSA-WSN) in a clustered wireless sensor network is proposed based on divisible load theory. The algorithm consists of two phases: intra-cluster task scheduling and inter-cluster task scheduling. Intra-cluster task scheduling deals with allocating different fractions of sensing tasks among sensor nodes in each cluster; inter-cluster task scheduling involves the assign-ment of sensing tasks among all clusters in multiple rounds to improve over-lap of communication with computation. OTSA-WSN builds from eliminating transmission collisions and idle gaps between two successive data transmissions. By removing performance degradation caused by communication interference and idle, the reduced finish time and improved network resource utilization can be achieved. With the proposed algorithm, the optimal number of rounds and the most reasonable load allocation ratio on each node could be derived. Finally, simulation results are presented to demonstrate the impacts of differ-ent network parameters such as the number of clusters, computation/commu-nication latency, and measurement/communication speed, on the number of rounds, makespan and energy consumption.
... It is unfeasible for a single node [5] to store, compute, and monitor the trust values with alteration of the whole network. To reduce the communication and memory overheads, we have scheduled the task using a well-known algorithm [6] and eliminate the unnecessary feedback from selfish nodes. Trust (in WSN or IoT) provides several advantages [7] and resolve various severe issues such as access control, protection from internal attacks, etc., [8][9][10][15][16][17][18] that cannot be solved using customary security solutions. ...
... In this research paper, we have employed an optimal task scheduling mechanism [6], authentication based data trust along with strong punishment and minimum reward with the robust trust model to detect and mitigate internal attacks as well as improve security, lifetime, and cooperation among sensor nodes. Moreover, a hybrid trust (communication and data) approach is used in a WSN clustered architecture as well as analyze of interdependency among trusted (genuine) sensor nodes. ...
... The proposed work employing clustering approach (refer to fig. 1) and an optimal task scheduling (OTS) algorithm [6] with a novel trust model (TSTM) to reduce communication (computation) overhead effectively. ...
Trust evaluation models are vital security enhancement tool for Wireless Sensor Networks (WSNs) to improve dependability (cooperation) among sensor nodes. This paper presents an Accurate and Efficient Distributed Trust Model for WSNs, which focus on fundamental requirements (resource efficiency) and security improvement. The proposed mathematical model (TSTM) computes trustworthiness of communication as well as data (transmitted) more accurately and precisely than other states of the art trust models such as LDTS, ADTC, and LWTM, etc. The proposed scheme consists of unique features like authentication based data trust, scheduler based node trust, and attack resistant by giving the high penalty and minimum reward during node misbehavior. The proposed trust model would be capable of providing security against various internal attacks due to consistency in trust values. Subject Classification: Primary 93A30, Secondary 49K15
... It is unfeasible for a single node [5] to store, compute, and monitor the trust values with alteration of the whole network. To reduce the communication and memory overheads, we have scheduled the task using a well-known algorithm [6] and eliminate the unnecessary feedback from selfish nodes. Trust (in WSN or IoT) provides several advantages [7] and resolve various severe issues such as access control, protection from internal attacks, etc., [8][9][10][15][16][17][18] that cannot be solved using customary security solutions. ...
... In this research paper, we have employed an optimal task scheduling mechanism [6], authentication based data trust along with strong punishment and minimum reward with the robust trust model to detect and mitigate internal attacks as well as improve security, lifetime, and cooperation among sensor nodes. Moreover, a hybrid trust (communication and data) approach is used in a WSN clustered architecture as well as analyze of interdependency among trusted (genuine) sensor nodes. ...
... The proposed work employing clustering approach (refer to fig. 1) and an optimal task scheduling (OTS) algorithm [6] with a novel trust model (TSTM) to reduce communication (computation) overhead effectively. ...
Trust evaluation models are vital security enhancement tool for Wireless Sensor
Networks (WSNs) to improve dependability (cooperation) among sensor nodes. This paper
presents an Accurate and Efficient Distributed Trust Model for WSNs, which focus on
fundamental requirements (resource efficiency) and security improvement. The proposed
mathematical model (TSTM) computes trustworthiness of communication as well as data
(transmitted) more accurately and precisely than other states of the art trust models such
as LDTS, ADTC, and LWTM, etc. The proposed scheme consists of unique features like
authentication based data trust, scheduler based node trust, and attack resistant by giving
the high penalty and minimum reward during node misbehavior. The proposed trust model
would be capable of providing security against various internal attacks due to consistency
in trust values.
... It is unfeasible for a single node [5] to store, compute, and monitor the trust values with alteration of the whole network. To reduce the communication and memory overheads, we have scheduled the task using a well-known algorithm [6] and eliminate the unnecessary feedback from selfish nodes. Trust (in WSN or IoT) provides several advantages [7] and resolve various severe issues such as access control, protection from internal attacks, etc., [8][9][10][15][16][17][18] that cannot be solved using customary security solutions. ...
... In this research paper, we have employed an optimal task scheduling mechanism [6], authentication based data trust along with strong punishment and minimum reward with the robust trust model to detect and mitigate internal attacks as well as improve security, lifetime, and cooperation among sensor nodes. Moreover, a hybrid trust (communication and data) approach is used in a WSN clustered architecture as well as analyze of interdependency among trusted (genuine) sensor nodes. ...
... The proposed work employing clustering approach (refer to fig. 1) and an optimal task scheduling (OTS) algorithm [6] with a novel trust model (TSTM) to reduce communication (computation) overhead effectively. ...
Trust evaluation models are vital security enhancement tool for Wireless Sensor Networks (WSNs) to improve dependability (cooperation) among sensor nodes. This paper presents an Accurate and Efficient Distributed Trust Model for WSNs, which focus on fundamental requirements (resource efficiency) and security improvement. The proposed mathematical model (TSTM) computes trustworthiness of communication as well as data (transmitted) more accurately and precisely than other states of the art trust models such as LDTS, ADTC, and LWTM, etc. The proposed scheme consists of unique features like authentication based data trust, scheduler based node trust, and attack resistant by giving the high penalty and minimum reward during node misbehavior. The proposed trust model would be capable of providing security against various internal attacks due to consistency in trust values.
... For instance, Dai et al. [14] presented a divisible task scheduling algorithm for clustered wireless sensor networks (DTAW) and a model to find the best task distribution among a set of actors. Similarly, an optimal task scheduling algorithm in a clustered WSN (OTSA-WSN) is presented in [15]. OTSA-WSN consists of two phases: 1) intra and 2) intercluster task scheduling. ...
... Nevertheless, the presented works do not support fault-tolerant techniques to improve reliability in combination with real-time requirements. Xie and Qin [17] and Zhang et al. [18] made a tradeoff between the schedule length and the energy depletion; their works have an additional advantage comparing with [14] and [15] by considering collaborative applications where tasks have precedence constraints. On the contrary, they do not consider a workload balancing mechanism. ...
A wireless sensor network (WSN) constitutes of nodes that are used to sense a certain physical environment phenomena and sends these collected data to a remote node for further processing and decision-making. With the proliferation of Internet of Things (IoT), WSN is evolving to be used as reliable, energy efficient, cost effective and scalable network infrastructure for many IoT applications. To have a robust IoT-WSN, it is a challenge to allocate the tasks among different sensor nodes because the distribution of dependent tasks among wireless nodes must satisfy precedence and time constraints, minimize energy consumption, and prevent signal interference. The problem becomes more complex if a system requires high reliability in the event of link or node failures. This paper presents an offline Discrete Particle Swarm Optimization algorithm for reliable Task Allocation (DPSO-TA) in IoT-WSN. DPSO-TA defines a utility function to optimise the task allocation problem by iteratively trying to improve the solution. The defined function is designed to satisfy goals including saving energy, reducing task completion time and minimizing the failure rates. A load balance mechanism is applied to make balancing of the tasks on several hosts. During the task allocation process, Frame Replication and Elimination for Reliability (FRER) approach is used to support flow fault tolerance by replicating transmitted flows over redundant routes. DPSO-TA considers the periodicity of the Time-Trigged (TT) flows and applies a physical interference model to prevent signal interference on the transmitted flows through assigning them to conflict-free timeslots. Analysis and simulation results show the optimization of DPSO-TA on timeliness, deadline missing ratio, failure rates and energy efficiency comparing it with algorithms that allocate tasks to hosts that produce the minimum completion time or that use a traditional procedure to find the most suitable nodes during the task allocation process.
... In addition to distribute the communication tasks, a group of studies focus on minimizing the execution time of the WSN sensing tasks to maximize the network lifetime [58,59,60,61], because quick response of a WSN could make for energy saving. They mainly aim at eliminating transmission collisions and idle gaps between two successive data transmissions for cluster-based WSNs. ...
... In WSNs, the time requirement is another important metric that needs to be considered when estimating the task allocation algorithms [58,31,90,66,69,73]. In many WSN applications, it is mandatory to quickly know the presence of some events to make a quick response. ...
Complex wireless sensor network (WSN) applications, such as those in Internet of things or in-network processing, are pushing the requirements of energy efficiency and long-term operation of the network drastically. Energy aware task allocation becomes crucial to extend the network lifetime, by efficiently distributing the tasks of applications among sensor nodes. Although task allocation has been deeply studied in wired systems, the resulting approaches are insufficient for WSNs due to limited battery resources and computing capability of WSN nodes, as well as the special wireless communication.
This work focuses on designing energy aware task allocation algorithms to extend the network lifetime of WSNs. More precisely, this work firstly proposes a centralized static task allocation algorithm ( CSTA ) for cluster based WSNs. Since a WSN application can be modeled by a directed acyclic graph (DAG), the task allocation problem is formulated as partitioning the modeled DAG graph into two subgraphs: one for the slave node and the other for the master node. By using a binary vector variable to represent the partition cut, CSTA formulates the problem of
maximizing network lifetime as a binary integer linear programming (BILP) problem. It provides one fixed time invariant partition cut (task allocation solution) for each slave node to balance the workload distribution of tasks. Moreover, motivated by the fact that using multiple partition cuts can achieve more balanced workload distribution, this work extends CSTA to a centralized dynamic task allocation algorithm, CDTA . By using a probability vector variable to stand for partition cuts with different weights, CDTA formulates the dynamic task allocation problem as
a linear programing (LP) problem. Due to the high complexity of centralized algorithms, this work further proposes a very lightweight distributed optimal on-line task allocation algorithm ( DOOTA ). Through an indepth analysis, it proves that the optimal task allocation solution consists of at most two partition cuts for each slave nodes. Based on this analysis, DOOTA enables each slave node to calculate its own optimal task allocation solution by negotiating with the master node with a very short time. These contributions significantly improve the application performance for WSNs, but also for other domains, e.g, mobile edge/fog computing.
Furthermore, the proposed task allocation algorithms are extended for different task scenarios and network structures, i.e., applications with conditional tasks, joint local and global appli-cations and multi-hop mesh network. Given a condition triggered application, it is modeled by a DAG graph with conditional branches. This conditional DAG is further decomposed into multiple stationary DAG graphs without conditional branches according to the satisfaction probability of each condition. Based on this modeling, a static and a dynamic condition triggered task allocation algorithms ( SCTTA and DCTTA ) are proposed by considering the multiple stationary DAG simultaneously. Targeting the joint local and global applications, this work designs a
static and a dynamic joint task allocation algorithms, SJTA and DJTA , based on BILP and LP, respectively. The modeling of local task allocation problem does not change, while the global task allocation problem is modeled by dividing the global DAG graph into different subgraphs mapping to the slave and master nodes. Besides the extensions for different task scenarios, this work presents a dynamic task allocation algorithm for multi-hop mesh networks ( DTA-mhop ) as well. The corresponding task allocation problem is modeled by dividing the DAG graph of each
sensor node into multiple subgraphs mapping to itself, the routing and sink nodes. By using the summation of assigned tasks for each node, DTA-mhop formulate the lifetime maximization as a LP problem.
The proposed task allocation algorithms are firstly evaluated using simulations and real WSN applications, in terms of network lifetime increase and algorithm runtime. In order to investigate the algorithm’s performance in realistic scenarios, the CSTA , CDTA and DOOTA algorithms are implemented in a real WSN based on the OpenMote platform. Both the simulation and implementation results show that the network lifetime can be dramatically extended. Remarkably, the network lifetime improvements are more significant for addressing complex applications. The proposed task allocation algorithms are therefore suitable for WSNs, and they can also be easily adapted to other wireless domains.
... The proposed trust estimation approach is simple, effective and practical since it detects and eliminates the faulty data from the networks. It is attack resilient and trustworthy with less overhead due to task scheduling algorithm (Dai et al., 2011). Figure 2 shows the flow chart of the proposed work. ...
Trust establishment (TE) among sensor nodes has become a vital requirement to improve security, reliability, and successful cooperation. Existing trust management approaches for large scale WSN are failed due to their low cooperation (i.e., dependability), higher communication and memory overheads (i.e., resource inefficient). This paper provides a new and comprehensive hybrid trust estimation approach for large scale WSN employing clustering to improve cooperation, trustworthiness, and security by detecting selfish sensor nodes with reduced resource (memory, power) consumption. The proposed scheme consists of unique features like authentication based data trust, scheduler based node trust, and attack resistant by giving the high penalty and minimum reward during node misbehavior. A task scheduling mechanism is employed for scheduling the significant task to reduce computation overhead. The proposed trust model would be capable to provide security against blackhole attack, grey hole attack, and badmouthing attack. Moreover, the proposed trust model feasibility has been tested with MATLAB. Simulation results exhibit the great performance of our proposed approach in terms of trust evaluation cost, prevention, and detection of malicious nodes with the help of analyzing consistency in trust values and communication overhead.
... The approach in [2] is based on a collaborative processing among nodes for task allocation adopting linear task clustering and a node assignment mechanism based on task duplication schemes. The model in [6] focuses on minimizing the task execution time in a clustered WSN. In [5], the authors propose a model for allocating the incoming tasks in WSN sensors according energy requirements. ...
... Example algorithms involve task clustering and node assignment mechanisms based on task duplication and migration schemes. In any case, the aim is to minimize the execution time, thus, to deliver the final response in limited time [18]. A model that could be adopted for such purposes is to cluster the network and build intra-cluster and intercluster scheduling relations. ...
In Internet of Things (IoT), numerous nodes produce huge volumes of data that are the subject of various processing tasks. Tasks execution on top of the collected data can be realized either at the edge of the network or at the Fog/Cloud. Their management at the network edge may limit the required time for concluding responses and return the final outcome/analytics to end-users or applications. IoT nodes, due to their limited computational and resource capabilities, can execute a limited number of tasks over the collected contextual data. A challenging decision is related to which tasks the IoT nodes should execute locally. Each node should carefully select such tasks to maximize the performance based on the current contextual information, e.g., tasks’ characteristics, nodes’ load and energy capacity. In this paper, we propose an intelligent decision making scheme for selecting the tasks that will be locally executed. The remaining tasks will be transferred to peer nodes in the network or the Fog/Cloud. Our focus is to limit the time required for initiating the execution of each task by introducing a two-step decision process. The first step is to decide whether a task can be executed locally; if not, the second step involves the sophisticated selection of the most appropriate peer to allocate it. When, in the entire network, no node is capable of executing the task, it is, then, sent to the Fog/Cloud facing the maximum latency. We comprehensively evaluate the proposed scheme demonstrating its applicability and optimality at the network edge.
... Recently, a great deal of studies and efforts have been conducted on task allocation in WSNs, and numerous fruitful achievements have been made. [5][6][7][8][9] Most existing task allocation strategies focus on how to distribute various subtasks to sensor nodes properly so that the network performance can be optimized. This issue is normally modeled as a multi-objective constrained optimization problem which has been proved to be non-deterministic polynomial-time (NP)-hard. ...
In a wireless sensor network, sensor nodes are strictly energy and capacity constrained, which makes it necessary for them to collaboratively execute a complex task. Thus, task allocation becomes a fundamental and crucial issue in wireless sensor networks. Most previous studies developed centralized methods to solve this problem. In addition, a common assumption is that all the sensor nodes are homogeneous, which is unfavorable in many real applications. In this article, a distributed task allocation strategy which can handle the problem in a heterogeneous wireless sensor network is proposed. The task is propagated from nodes to nodes and each node matches its own capacity with the required capacities until all the demanded capacities of the task are obtained. Building on this, an enhanced task allocation strategy based on self-organization is developed. By utilizing previous assigning information, the nodes with proper capacities will be selected as candidate nodes, then the paths to these nodes will be optimized. In so doing, a new arriving task can be allocated directly and quickly. Simulation results show the feasibility of the proposed approach. Furthermore, the overall performance of the self-organization-based strategy is validated through a comparison with a particle swarm optimization–based centralized method and the fundamental method.
... The primary objective of task scheduling in wireless sensor networks is to find an optimal strategy of splitting the original tasks received by SINK into a number of sub-tasks as well as distributing these sub-tasks to the sensors in the right order. The directed acyclic graph [1], independent task sets [2] and divisible load theory [3] are usually used as modeling tools for task scheduling in wireless sensor networks, but these models only take the makespan as the main objective, and assign the task to sensors. However, wireless sensor networks are widely applied to both abominable and military environments. ...
Sensing tasks should be allocated and processed among sensor nodes in minimum times so that users can draw useful conclusions through analyzing sensed data. Furthermore, finishing sensing task faster will benefit energy saving. The above needs form a contrast to the lower efficiency of task-performing caused by the failureprone sensor. To solve this problem, a multi-objective optimization algorithm of task scheduling is proposed for wireless sensor networks (MTWSN). This algorithm tries its best to make less makespan, but meanwhile, it also pay much more attention to the probability of task-performing and the lifetime of network. MTWSN avoids the task assigned to the failure-prone sensor, which effectively reducing the effect of failed nodes on task-performing. Simulation results show that the proposed algorithm can trade off these three objectives well. Compared with the traditional task scheduling algorithms, simulation experiments obtain better results.
... In addition to the above differences, the SCAs in WSNs are faced with the ever-changing user requests, so they must have the ability to apperceive any changes in the outside environment, and dynamically evolve to adapt to these changes. In order to provide better reliability and performance to users, the SCAs in WSNs must have more adaptability to collect various changes in real-time, to adjust themselves online in runtime [18,19]. ...
The services composition technology provides flexible methods for building service composition applications (SCAs) in wireless sensor networks (WSNs). The high reliability and high performance of SCAs help services composition technology promote the practical application of WSNs. The optimization methods for reliability and performance used for traditional software systems are mostly based on the instantiations of software components, which are inapplicable and inefficient in the ever-changing SCAs in WSNs. In this paper, we consider the SCAs with fault tolerance in WSNs. Based on a Universal Generating Function (UGF) we propose a reliability and performance model of SCAs in WSNs, which generalizes a redundancy optimization problem to a multi-state system. Based on this model, an efficient optimization algorithm for reliability and performance of SCAs in WSNs is developed based on a Genetic Algorithm (GA) to find the optimal structure of SCAs with fault-tolerance in WSNs. In order to examine the feasibility of our algorithm, we have evaluated the performance. Furthermore, the interrelationships between the reliability, performance and cost are investigated. In addition, a distinct approach to determine the most suitable parameters in the suggested algorithm is proposed.
... Recent approximations are presented by Dai, which allow a reasonable but static approximation for time variable strategy. Also, Kim [20] have followed a Maximum Allowable Time Delay (MADB), where complex task behaviour is permitted as long as MADB is preserved [22] [23]. 366 P.Q. ...
This paper presents a fuzzy control approach to a helicopter MIMO nonlinear system, implemented on a Networked Control System, as case study. For this, a hardware-in-the-Loop implementation is developed using several multi-channel A/D Cards, integrated to a computer network system. Variant time delays are considered over Ethernet and CANBUS networks. Fuzzy logic is used to deal with the complexity of the integrated computer network as well as with the dynamics of the system. Two fuzzy logic control systems are coupled for both signals of the helicopter case study: yaw and pitch. Both these tend to concentrate around desired references, considering variant time delays.
... The reliability and performance of WSA is of great concern. Usually, the measure of performance is the task execution time (service time) [20,21]. This index can be significantly improved using the WSB that divides a task into a set of ASs, which can be executed in parallel by some SNs. ...
Services composition technology provides a flexible approach to building Wireless Sensor Network (WSN) Service Applications (WSA) in a service oriented tasking system for WSN. Maintaining the data security of WSA is one of the most important goals in sensor network research. In this paper, we consider a WSN service oriented tasking system in which the WSN Services Broker (WSB), as the resource management center, can map the service request from user into a set of atom-services (AS) and send them to some independent sensor nodes (SN) for parallel execution. The distribution of ASs among these SNs affects the data security as well as the reliability and performance of WSA because these SNs can be of different and independent specifications. By the optimal service partition into the ASs and their distribution among SNs, the WSB can provide the maximum possible service reliability and/or expected performance subject to data security constraints. This paper proposes an algorithm of optimal service partition and distribution based on the universal generating function (UGF) and the genetic algorithm (GA) approach. The experimental analysis is presented to demonstrate the feasibility of the suggested algorithm.
Current advances in the Internet of Things (IoT) and Edge Computing (EC) involve numerous devices/nodes present at both ‘layers’ being capable of performing simple processing activities close to end users. This approach targets to limit the latency that users face when consuming the provided services. The minimization of the latency requires for novel techniques that deliver efficient schemes for tasks management at the edge infrastructure and the management of the uncertainty related to the status of edge nodes during the decision making as proposed in this paper. Tasks should be executed in the minimum time especially when we aim to support real time applications. In this paper, we propose a new model for the proactive management of tasks’ allocation to provide a decision making model that results the best possible node where every task should be executed. A task can be executed either locally at the node where it is initially reported or in a peer node, if this is more efficient. We focus on the management of the uncertainty over the characteristics of peer nodes when the envisioned decisions should be realized. The proposed model aims at providing the best possible action for any incoming task. For such purposes, we adopt an unsupervised machine learning technique. We present the problem under consideration and specific formulations accompanied by the proposed solution. Our extensive experimental evaluation with synthetic and real data targets to reveal the advantages of the proposed scheme.
Current advances in the Internet of Things (IoT) and Edge Computing (EC) involve numerous devices/nodes present at both 'layers' that are capable of performing simple processing activities close to end users. This approach targets to limit the latency that users face when consuming the desired services. The minimization of the latency requires for novel techniques that deliver efficient schemes for tasks management. Tasks should be executed in the minimum time especially when we aim to support real time applications. In this paper, we focus on the edge infrastructure and propose a new model for the proactive management of tasks' allocation to provide a decision making model that results the best possible node where every task should be executed. A task can be executed either locally at the node where it is initially reported or in a peer node, if this is more efficient. We focus on the management of the uncertainty over the characteristics of peer nodes when the envisioned decisions are realized. The proposed model aims at providing the best possible action for any incoming task. For such purposes, we adopt an unsupervised machine learning technique. We present the problem under consideration and specific formulations accompanied by the proposed solution. Our extensive experimental evaluation with synthetic and real data targets to reveal the advantages of the proposed scheme.
Complex wireless sensor network applications as those in Internet of Things or in-network processing are pushing the requirements for energy efficiency and data processing drastically. Executing the tasks of such complex applications in a single node may lead it to die soon, since the nodes in WSNs are usually with limited and generally irreplaceable power sources. How to distribute the tasks across the network and simultaneously balance the energy consumption of each node to achieve energy efficiency and to extend the network lifetime are crucial and urgent requirements in WSNs. Energy-aware task allocation (sometimes also called workload distribution) technologies, which have been deeply studied in multiprocessor systems, grid computing, and system on chip (SoC), are attracting the attention of the research community in WSNs. Due to the limited energy source and computing capability as well as the wireless communication, the task allocation problem in WSNs is different from traditional wired systems. This chapter provides an application-level taxonomy and an in-depth review of task allocation approaches in WSNs. It enables the readers to gain a clear view of current task allocation approaches, by taking the evaluation metrics and the modeling methods of the problem into account.
By allocating a set of tasks onto a set of nodes and adjusting the execution time of tasks, task mapping is an efficient approach to realize distributed computing. Cyber-Physical Systems (CPS), as a particular case of distributed systems, raise new challenges in task mapping, because of the heterogeneity and other properties traditionally associated with Wireless Sensor and Actuator Networks (WSAN), including shared sensing, acting and real-time computing. In addition, many of the real-time tasks of CPS can be executed in an imprecise way. Such systems accept an approximate result as long as the baseline Quality-of-Service (QoS) is satisfied and they can execute more computations to yield better results, if more system resources is available. These systems are typically considered under the Imprecise Computation (IC) model, achieving a better tradeoff between QoS and limited system resources. However, determining a QoS-aware mapping of these real-time IC-tasks onto the nodes of a CPS creates a set of interesting problems. In this paper, we firstly propose a mathematical model to capture the dependency, energy and real-time constraints of IC-tasks, as well as the sensing, acting, and routing in the CPS. The problem is formulated as a Mixed-Integer Non-Linear Programming (MINLP) due to the complex nature of the problem. Secondly, to efficiently solve this problem, we provide a linearization method that results in a Mixed-Integer Linear Programming (MILP) formulation of our original problem. Finally, we decompose the transformed problem into a task allocation subproblem and a task adjustment subproblem, and, then, we find the optimal solution based on subproblem iteration. Through the simulations, we demonstrate the effectiveness of the proposed method.
In Internet of Things (IoT), numerous nodes produce huge volumes of data that are subject of various processing tasks. Tasks execution on top of the collected data can be realized either at the edge of the network or at the Fog/Cloud. Their management at the network edge may limit the required time for concluding responses and return the final outcome/analytics to end-users or applications. IoT nodes, due to their limited computational and resource capabilities, can execute a limited number of tasks over the collected contextual data. A challenging decision is related to which tasks IoT nodes should execute locally. Each node should carefully select such tasks to maximize the performance based on the current contextual information, e.g., tasks' characteristics, nodes' load and energy capacity.
In this paper, we propose an intelligent decision making scheme for selecting the tasks that will be locally executed. The remaining tasks will be transferred to peer nodes in the network or the Fog/Cloud. Our focus is to limit the time required for initiating the execution of each task by introducing a two-step decision process. The first step is to decide whether a task can be executed locally; if not, the second step involves the sophisticated selection of the most appropriate peer to allocate it. When, in the entire network, no node is capable of executing the task, it is, then, sent to the Fog/Cloud facing the maximum latency. We comprehensively evaluate the proposed scheme demonstrating its applicability and optimality at the network edge.
Given temporal variability of farmland, the coverage rate of wireless sensor network (WSN) is usually low. There may arise the problems of blind spot area and congestion of hot spots. We propose a coverage strategy for heterogeneous nodes in WSN based on temporal variability of farmland, which predicts the key nodes using key node prediction model according to temporal variability of farmland environment. Through introduction of renewable energy nodes, the positions of heterogeneous nodes in the network can be determined. The task is re-allocated to the heterogeneous nodes depending on the residual energy of nodes, and the state of nodes in the network is adjusted dynamically. Simulation shows that CSHN can effectively reduce the node death rate and network energy consumption, while prolonging the survival of network and equalizing coverage and network energy consumption.
Aiming at the task allocation problem for cooperatively acquiring complex urban traffic parameters in urban road traffic information acquisition sensor network, the sensor network is mapped into multi-agent system; taking the task execution time, node energy consumption and network load balance as the evaluation functions, adopting coalition based cooperative method, the nonlinear multi-objective optimization model of task allocation of sensor network is constructed. Genetic simulated annealing algorithm is used to search the optimal coalition model and the task allocation strategy optimization is achieved. The simulation experiments in the real environment of urban road traffic information acquisition were carried out. The simulation results show that the proposed algorithm has the ability to optimize the coalition model of task allocation effectively. Compared with other optimization algorithms, the fitness function value of the optimal coalition model is low, the task execution time is short and the network energy consumption is low. The proposed coalition model and algorithm are feasible for the task allocation problem of cooperative detection in wireless sensor networks, for urban traffic information acquisition.
One of challenging issues for task allocation problem in wireless sensor networks (WSNs) is distributing sensing tasks rationally among sensor nodes to reduce overall power consumption and ensure these tasks finished before deadlines. In this paper, we propose a soft real-time fault-tolerant task allocation algorithm (FTAOA) for WSNs in using primary/backup (P/B) technique to support fault tolerance mechanism. In the proposed algorithm, the construction process of discrete particle swarm optimization (DPSO) is achieved through adopting a binary matrix encoding form, minimizing tasks execution time, saving node energy cost, balancing network load, and defining a fitness function for improving scheduling effectiveness and system reliability. Furthermore, FTAOA employs passive backup copies overlapping technology and is capable to determinate the mode of backup copies adaptively through scheduling primary copies as early as possible and backup copies as late as possible. To improve resource utilization, we allocate tasks to the nodes with high performance in terms of load, energy consumption, and failure ratio. Analysis and simulation results show the feasibility and effectiveness of FTAOA. FTAOA can strike a good balance between local solution and global exploration and achieve a satisfactory result within a short period of time.
A variety of optimization algorithms has been developed for non-linear and non-convex problems in which numerous reconfigurable sensors need to be assigned to many tasks. The algorithms are based on modified gradient-search methods and inspired by centralized/distributed principles. Numerical evaluation of these algorithms on a statistically large set of optimization problems has shown that while each particular algorithm does not necessarily provide the optimal solution in all possible cases, some are very efficient in solving them. Distributed (agent-based) approaches are usually advocated because of their scalability and speed, but the developed centralized (synchronous) algorithms are shown to be better in terms of speed, and simultaneously in terms of effectiveness, and therefore, in terms of efficiency.
In recent times the interest in wireless sensor mesh networks has grown considerably from personal, to local and metropolitan areas deployment. These networks consist of several mesh routers with minimal mobility and mesh clients that can be either mobile or stationary. The clients may also form a client mesh network among themselves and with routers. The various nodes over the network are interconnected via wireless links which might possibly employ multiple radio interfaces. One major attribute of such networks is the presence of redundant links which removes the single point failure that is present in the classical star or tree networks. Most researches in this field focus on the study of various routing protocols while we sought to introduce the application divisible load theory to find an optimum data aggregation strategy that optimize the networks performance with respect to response time and network delay. We define data aggregation as the process of data sensing and reporting back to the sink nodes, typically routers. The performance of wireless mesh network with 25 sensor nodes is examined by varying network bandwidth and sensing power of sensor nodes. Basic recursive equations for sensing and data reporting are developed for the case of homogeneous and heterogeneous mesh networks and the performance results of two representative data sensing and reporting strategies are presented.
Divisible load theory is a methodology involving the linear and continuous modeling of partitionable computation and communication loads for parallel processing. It adequately represents an important class of problems with applications in parallel and distributed system scheduling, various types of data processing, scientific and engineering computation, and sensor networks. Solutions are surprisingly tractable. Research in this area over the past decade is described.
One of the challenge in developing smart sensor networks is the minimization of network delay or at the very least be able to have upper and lower boundaries of network delay when sensor nodes respond to higher level applications. In this paper, we present a highly efficient task scheduling method based on linear programming that integrates both sensing and networking communication delay. The objective is to minimize the total response time and global power consumption of the network with respect to the total number of sensor nodes in the network. Simulation results based on closed-form solutions for the task scheduling problem are presented for two scenarios with homogeneous and six scenarios with heterogeneous sensor nodes using single level tree-network topology. Specifically, for the heterogeneous scenarios, responding sequence that results in global optimum total respond time has also been found.
Networking together hundreds or thousands of cheap microsensor nodes allows users to accurately monitor a remote environment by intelligently combining the data from the individual nodes. These networks require robust wireless communication protocols that are energy efficient and provide low latency. We develop and analyze low-energy adaptive clustering hierarchy (LEACH), a protocol architecture for microsensor networks that combines the ideas of energy-efficient cluster-based routing and media access together with application-specific data aggregation to achieve good performance in terms of system lifetime, latency, and application-perceived quality. LEACH includes a new, distributed cluster formation technique that enables self-organization of large numbers of nodes, algorithms for adapting clusters and rotating cluster head positions to evenly distribute the energy load among all the nodes, and techniques to enable distributed signal processing to save communication resources. Our results show that LEACH can improve system lifetime by an order of magnitude compared with general-purpose multihop approaches.
An optimal load allocation approach is presented for measurement and data reporting in wireless sensor networks with a single level tree network topology. The measurement problem investigated involves a measurement space, part of which can be sampled by each sensor. We seek to optimally assign sensors part of the measurement space to minimize reporting time and energy usage. Three representative measurement and reporting strategies are studied. This work is novel as it considers, for the first time, the measurement capacity of processors and assumes negligible computation time which is radically different from the traditional divisible load scheduling research to date. Aerospace applications include satellite remote sensing and monitoring and sensor networks deployed and monitored from the air.
Optimal data scheduling strategies in a hierarchical wireless sensor network (WSN) are considered. Data aggregation in clusterheads is considered to find a closed form solution for the optimal amount of data that is to be reported by each sensor node using a newly introduced parameter, information utility. The optimal conditions for the feasible measurement instruction assignment time and for the minimum round time are derived and examined. Based on the optimal conditions, a performance evaluation is demonstrated via simulation study.
Due to uncertainties in target motion and limited sensing regions of sensors, single-sensor-based collaborative target tracking in wireless sensor networks (WSNs), as addressed in many previous approaches, suffers from low tracking accuracy and lack of reliability when a target cannot be detected by a scheduled sensor. Generally, actuating multiple sensors can achieve better tracking performance but with high energy consumption. Tracking accuracy, reliability, and energy consumed are affected by the sampling interval between two successive time steps. In this paper, an adaptive energy-efficient multisensor scheduling scheme is proposed for collaborative target tracking in WSNs. It calculates the optimal sampling interval to satisfy a specification on predicted tracking accuracy, selects the cluster of tasking sensors according to their joint detection probability, and designates one of the tasking sensors as the cluster head for estimation update and sensor scheduling according to a cluster head energy measure (CHEM) function. Simulation results show that, compared with existing single-sensor scheduling and multisensor scheduling with a uniform sampling interval, the proposed adaptive multisensor scheduling scheme can achieve superior energy efficiency and tracking reliability while satisfying the tracking accuracy requirement. It is also robust to the uncertainty of the process noise.
The task scheduling in the network demands as far as possible the shortest task completion time, the lowest energy consumption and the highest balanced use of energy under limited energy of nodes. Therefore, traditional multiprocessor Directed Acyclic Graph (DAG) scheduling algorithm can not be directly applied to sensor task scheduling. This paper proposes an Energy Balanced DAG Task Scheduling algorithm for Wireless sensor network (EBDAG_WSN). Its main idea is to get initial Chromosome by heuristic optimization algorithms. By redefinition of operations in the Genetic Algorithm (GA), the task scheduling in WSN is optimized synthetically. Through simulations based on randomly generated task graphs, experiment results show that combining heuristic optimization algorithm with bionic algorithm, the optimization technology has good real-time performance and high efficiency of energy.
This paper exploits the tradeoff between data quality and energy consumption to extend the lifetime of wireless sensor networks. To obtain an aggregate form of sensor data with precision guarantees, the precision constraint is partitioned and allocated to individual sensor nodes in a coordinated fashion. Our key idea is to differentiate the precisions of data collected from different sensor nodes to balance their energy consumption. Three factors affecting the lifetime of sensor nodes are identified: 1) the changing pattern of sensor readings; 2) the residual energy of sensor nodes; and 3) the communication cost between the sensor nodes and the base station. We analyze the optimal precision allocation in terms of network lifetime and propose an adaptive scheme that dynamically adjusts the precision constraints at the sensor nodes. The adaptive scheme also takes into consideration the topological relations among sensor nodes and the effect of in-network aggregation. Experimental results using real data traces show that the proposed scheme significantly improves network lifetime compared to existing methods.
In the divisible load distribution, the classic methods on linear arrays divide the computation and communication processes into multiple time intervals in a pipelined fashion. K.Li (1998) has proposed a set of improved algorithms for linear arrays that can be generalized to k -dimensional meshes. In this paper, we first propose the algorithm M (multi-installment) that employs the multi- installment technique to improve the best algorithm Q proposed by Li. Second, we propose the algorithm S (start-up cost), which includes the computation and communication start-up costs in the design. Although the asymptotic speed-ups of our algorithms M and S derived from the closed-form solutions are the same as algorithm Q, our algorithms approach the optimal speed-ups considerably faster than algorithm Q as the number of processors increases. Finally, we combine algorithms M and S and propose the algorithm MS. Although algorithm MS has the same the asymptotic performance as algorithms Q and S, it achieves a better speed-up when the load to be processed is very large and the number of processors is fixed or when the load to be processed is fixed and the number of processors is small.
Divisible load applications occur in many fields of science and engineering and can be easily parallelized in a master-worker fashion, but pose several scheduling challenges. While a number of approaches have been proposed that allocate load to workers in a single round, using multiple rounds improves overlap of computation with communication. Unfortunately, multiround algorithms are difficult to analyze and have thus received only limited attention. In this paper, we answer three open questions in the multiround divisible load scheduling area: 1) how to account for latencies, 2) how to account for heterogeneous platforms, and 3) how many rounds should be used. To answer 1), we derive the first closed-form optimal schedule for a homogeneous platform with both computation and communication latencies, for a given number of rounds. To answer 2) and 3), we present a novel algorithm, UMR. We evaluate UMR in a variety of realistic scenarios.