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* Dr. R. S. Gad ,Associate Professor, Department of Electronics, Goa University , Goa, India
403206 .
Kaivalyam1: Kaivalyam, is the ultimate goal of yoga; a nd means "Mukti" or "detachment‖ for absolute freedom. It is
said in Vedas / Upanishads that there re 4 types of Mukthi/Kaivalyam. Namely: Saalokya, Saaroopya, Saameepya, &
Saayujya. Which can be explain as when person worships a Deity he attains to the world of that Deity called ‗Saalokya‘;
further he attains ‗Saaroopya‘ i.e. form o f that Deity; then he attains ‗Saameepya‘ i.e. proximity to that Deity and finally he
becomes one w ith that Deity i.e. ‗Saa yujya‘ (http://www.namadwaar.org/nibbles/?p=150). This way the person attains t he
absolute freedom which is similar to ‗Thing‘ in IoT getting freedom in space
Reliable and Scalable Architecture for Internet of Things
for Sensors Using Soft-Core Processor
U. V. Rane, V. R. Gad, R. S. Gad*, G. M. Naik
ALTERA SoC laboratory, Department of Electronics, Goa University, Goa , India
{udaysingvrane@gmail.com, gadvinaya@gmail.com,
rsgad@unigoa.ac.in, gmnaik@unigoa.ac.in}
Abstract. With significant technological developments as well as advances in
sensors, wireless communications and Internet – a lot of research areas have
emerged, such as wearable computing, context-aware homes, mobile phone
sensing and smart vehicle systems. From those emerging areas, there is a clear
trend to augment the physical devices/objects with sensing, computing & com-
munication capabilities, connect them together to form a network and make use
of the collective effect of networked smart things − the Internet of
Things(IoT). This paper proposes the IoT architecture ―Kaivalyam1‖ for sen-
sors/actuator. The configurable interface generates the identity of the ‗Thing‘
and stores in 32-bit datagram. The various configuration of the 32-bit datagram
from 4,8,12 and 16-bit device data and respective 14, 12, 10 and 8-bits dupli-
cate identifier can scale the number of devices connected to design platform.
These datagram‘s read into FIFO of the Triple Speed Ethernet (TSE) for trans-
mission using Low Density Parity Check (LDPC) encoder. Simulation studies
for the system were performed for block length 512 bits, which is the minimum
Ethernet frame length of 64 bytes. AWGN(Additive White Gaussian Noise) is
introduced in the channel and BER(Bit Error Rate) is computed for different
(1dB to 6dB) SNR(Signal to Noise Ratio) showing BER of 10-4 -4 to 10-5 can
be achieved for SNR of 2.5 dB indicating the secured and reliable data trans-
mission.
Keywords: Index Terms— Internet of Things, Architecture, Scheduling, Ether-
net and LDPC.
1 INTRODUCTION
―All things appear and disappear because of the concurrence of causes and cond itions.
Nothing ever exists entirely alone; everything is in relation to everything else‖ is what
said by Prince Gautama Siddharta[1], which has direct relevance with the idea of
Internet of Things (IoT) introduced in the PCANS Model[2]. The PCANS model
suggests that ―all systems are structured along these three domains, Individuals,
Tasks, and Resources‖ and also introduces the concept that networks occur across
multiple domains and that they are interrelated. Network science studies complex
networks such as engineered, information, biological, cognitive, semantic and social
networks. The field draws on theories and methods including graph theory from
mathematics, statistical mechanics from physics, data mining and information visuali-
zation from computer science, inferential modeling from statistics and social structure
from sociology. A telecommunications network is a collection of terminals, links and
nodes which connect to enable telecommunication between users of the terminals.
Each terminal in the network has a unique address so messages or connections can be
routed to the correct recipients. The links connect the nodes together and are them-
selves built upon an underlying transmission network which physically pushes the
message across the link; using circuit switched, message switched or packet switched
routing. Examples of telecommunications networks are: computer networks, the In-
ternet, the telephone network, the global Telex network, the aeronautical ‗ACARS‘
network.
IoT works has been proposed in several application scenarios, such as environmen-
tal monitoring, e-health, intelligent transportation systems, military, and industrial
plant monitoring. Technically this requires embedding sensing, actuation, processing,
securing, and reliable networking into common objects. Sensors device availability
and decreased cost of hardware has triggered the era of new computing systems to
integrate the vehicles, devices, goods and everyday‘s object to be a part of IoT[3].
This paper proposes IoT architecture based on the extended Ethernet MAC ap-
proach providing various communication front-ends interfaces to reconfigure them for
the propose common ‗Kaivalyam1‘ interface which explore novel way for person to
object and object to object communication like the sensors or actuator network inter-
face on IoT platform sensors/actuator. Further it elaborates the capabilities of archi-
tectures for scalability for nodes and reliability of data using Error Correction Coding
with LDPC coding schemes.
2 Architecture of IoT and related enabling technologies and
standardizations
There are three IoT components which enables seamless ubicomp: (a) Hardware—
made up of sensors, actuators and embedded communication hardware (b) Middle-
3
ware—on demand storage and computing tools for data analytics and
(c)Presentation—novel easy to understand visualization and interpretation tools which
can be widely accessed on different platforms and which can be designed for different
applications. Gartner 2012 Hype Cycle of expected emerging technologies indicates
the IoT will take 10-12 years to reach to plateau of productivity[4] . The claims of the
Gartner are justified by the ongoing status of standardization activities of EPCglobal,
GRIFS, M2M, 6LoWPAN, ROLL and other stakeholders of IoT. Most of them are
supporting the data rate of 102 kbps over 1- 100 meters range of communication.
2.1 Enabling Technologies IoT
Development of certain enabling technologies such as nano-electronics, communica-
tions, sensors, smart phones, embedded systems, cloud computing and software tech-
nologies will be essential to support important future IoT product innovations affect-
ing the different industrial sectors. In addition, systems and network infrastructure
(Future Internet) are becoming critical due to the fast growth and advanced nature of
communication services as well as the integration with the healthcare systems,
transport, energy efficient buildings, smart grid, smart cities, and electric vehicles
initiatives.
2.1.1 More Than Moore (MtM)
Since 2007, the ITRS (The International Technology Roadmap For Semiconductors:
2012 Update) has addressed the concept of functional diversification under the title
―More than Moore‖ (MtM). The MtM approach typically allows for the non-digital
functionalities which don‘t scale as per ‗Moor‘s Law‘ (e.g., RF communication, pow-
er control, passive components, sensors, actuators) to migrate from the system board-
level into a particular package-level (SiP) or chip-level (SoC) system solution. The
basic idea of MtM is the pervasive presence around us of a variety of things or objects
which, through unique addressing schemes, are able to interact with each other and
cooperate with their neighbors to reach common goals [5]. With regards to the IoT
paradigm at large, a very interesting standardization effort is now starting in ETSI [6]
(the European Telecommunications Standards Institute), that has purpose of conduct-
ing standardization activities relevant to M2M systems and sensor networks (in the
view of the IoT). The goals of the ETSI M2M committee include: the development
and the maintenance of an end-to-end architecture for M2M (with end-to-end IP phi-
losophy behind it), strengthening the standardization efforts on M2M, including sen-
sor network integration, naming, addressing, location, QoS, security, charging, man-
agement, application, and hardware interfaces [7]. Presently, Micro-electro-
mechanical systems (MEMS) technologies can fabricate micrometer-sized mechanical
structures (suspended bridges, cantilevers, membranes, fluid channels, etc.) that are
often integrated with analog and digital circuitry. MEMS can act as sensors, receiving
information from their environment, or as actuators, responding to a decision from a
control system to change the environment. It also reviews emerging MEMS applica-
tions, including optical filters, picoprojectors, the electronic nose, microspeakers, and
ultrasound devices.
2.1.2 Communication technologies
Internet Protocol is used in network technology for connecting smart objects around
the world. According to the Internet Protocol for Smart Objects (IPSO) vision, the IP
stack is a light protocol that already connects a huge amount of communicating devic-
es and runs on tiny and battery operated embedded devices. This guarantees that IP
has all the qualities to make IoT a reality. By reading IPSO whitepapers, it seems that
through a wise IP adaptation and by incorporating IEEE 802.15.4 into the IP architec-
ture, in the view of 6LoWPAN [8], the full deployment of the IoT paradigm will be
automatically enabled. We have proved the same for smart system control platform
for Ethernet enabled devices [9]. As this represents only a partial functional require-
ment in the IoT, similar to the role of communication technology in the Internet and
equaling communication technologies such as WiFi, Bluetooth, ZigBee, I2C, CAN,
6LoWPAN, ISA 100, WirelessHart /802.15.4, 18000-7, LTE to the Internet of Things
is too simplistic. The 6LoWPAN concept originated from the idea that low-power
devices with limited processing capabilities should be able to participate in
the Internet of Things. Wireless sensor network and MANET is capable of performing
various mechanisms [10] such as self-configuration , multi hop communication , en-
ergy efficient operations, in network processing, data centric and content-based net-
working, exploiting location and activity pattern , positioning, scheduling, time syn-
chronization topology control and routing. However, we can say that these technolo-
gies certainly might be part of Internet of Things.
2.1.3 Addressing and networking issues
The IoT will include an incredibly high number of Nodes. Currently, the IPv4 proto-
col identifies each node through a 4-byte address and these addresses are depleting
rapidly and will soon reach zero. IPv6 addressing has been proposed for low-power
wireless communication nodes within the 6LoWPAN context. IPv6 addresses are
expressed by means of 16 bytes to define 1038 addresses, which should be enough to
identify any object which is worth to be addressed. IPv6 addresses are assigned to
organizations in much larger blocks as compared to IPv4 address assignments—the
recommended allocation is a /48 block which contains 280 addresses, being 248 or
about 2.8×1014 times larger than the entire IPv4 address space of 232 addresses and
about 7.2×1016 times larger than the /8 blocks of IPv4 addresses, which are the larg-
est allocations of IPv4 addresses. The total pool, however, is sufficient for the fore-
seeable future, because there are 2128 or about 3.4×1038 (340 trillion trillion tril-
lion) unique IPv6 addresses. This address space is many times that of the world popu-
lation of 7 billion which will accommodate the generation of huge quantities of data
over IoT, between 1.000 and 10.000 per person per day [11].
5
2.1.4 Embedded devices:
RFID or wireless sensor networks (WSN), may be part of the Internet of Things, but
as standalone applications (intranets) they miss the back-end information infrastruc-
tures necessary to create new services. The IoT has come to mean much more than
just networked RFID systems. While RFID systems have at least certain standardized
information architectures to which all the Internet community could refer, global
WSN infrastructures have not yet been standardized. An IoT vision statement, which
goes well beyond a mere ‗‗RFID centric‖ approach, is also proposed by the consorti-
um CASAGRAS [12]. With regards to the RFID technology, it is currently slowed
down by fragmented efforts towards standardization, which is focusing on a couple of
principal areas: RFID frequency and readers-tags (tags-reader) communication proto-
cols, data format placed on tags and labels. The major standardization bodies dealing
with RFID systems are EPCglobal, ETSI, and ISO.
2.2 Internet of Things Architecture Technology
RFID-installations in production and logistics today can be considered as an Intranet
of Things or Extranet of Things. Traditional communication means, such as
EDIFACT (Electronic Data Interchange For Administration, Commerce and
Transport ), are used to communicate with a limited number of preferred partners.
These early approaches need to be extended to support open Internet architectures.
Most of the RFID installations introduce a novel read-out method for a hierarchical
wireless master slave RFID reader architecture of multi standard Near Field Commu-
nication (NFC) and Ultra High Frequency (UHF) technologies. This can be used to
build a smart home service system those benefits in terms of cost, energy consump-
tion and complexity [13].
There are several projects and standardization initiatives on sensor networks, which
may eventually converge with the IoT. The core objective of the COBIS project was
to provide the technical foundation for embedded and wireless sensor network tech-
nology in industrial environments. SENSEI creates an open, business-driven architec-
ture that fundamentally addresses the scalability problems for a large number of glob-
ally distributed wireless sensors and actuator devices. It provides network and infor-
mation management services to enable reliable and accurate contextual information
retrieval and interaction with the physical environment.
Likewise, other smaller research projects exist, such as GSN[14], SARIF[15], and
MoCoSo, that combine concepts of object identification, sensor data and the Internet.
Sensor networks can be integrated in the IoT for example, by integration with the
EPCglobal Architecture Framework. Although the EPCglobal Network does not yet
provide adequate support for the inclusion of sensor values in the streams of data, the
Action Groups inside the GS1/EPCglobal community are actively researching issues
such as ‗Active Tagging‘ and ‗Sensor and Battery Assisted Passive Tags‘. The EPC
Sensor Network[16] is an effort of the Auto-ID Lab in Korea to incorporate Wireless
Sensor Networks (WSN) and sensor data into the EPCglobal Network architecture
and standards. While identification, sensing and actuator integration are core func-
tionalities in an IoT, there are further requirements such as scalability and robustness
that need to be addressed.
3 Architecture ‘Kaivalyam’ for IoT using Ethernet MAC
backbone
Open standards are required to use and extend its functionality. It will be a huge net-
work, considering that every object has its virtual representation. Therefore, scalabil-
ity is required. The Internet of Things will need to be flexible enough to adapt to
changing requirements and technological developments. Proposed architecture (Fig.
1.) support the flexibility as one can add as many communication interfaces develop-
ing in near future. Also the protocols can be coded decoded at the Kaivalyam plat-
form. The architecture for the sensor integration should address issue like Unique
Identity, Integration of dynamic data, Support for non-IP devices, Integration of an
actuator interface, Data synchronization for offline support, Optional Interface for
software agents etc. Our architecture supports almost all these features except the last.
The Optional interface (Fig. 2.) can be also provided using the soft-core NIOS proces-
sor System on Chip (SoC) solution by adding the respective lightweight IP protocols
in the system software of the SoC[9]. We have proposed here the 4-port switch having
soft-core processor for monitoring and routing the packets.
The data is encoded using LDPC (Low Density Parity Check) codes which are trans-
mitted over usually the Gaussian channel after modulation. The data transmitted is
demodulated and then decoded for errors corrections, if any.
3.1 Extended MAC Network Interface Card Architecture ‘Kaivalyam’ for
IoT.
Wireless sensor network solutions are based on the IEEE 802.15.4 standard, which
defines the physical and MAC layers for low-power, low bit rate communications in
wireless personal area networks(WPAN) [17].WSN is capable of performing various
mechanisms such as self-configuration, multi-hop communication, energy efficient
operations, in-network processing, data centric and content based networking, sched-
uling, time synchronization topology control and routing. IEEE 802.15.4 does not
include specifications on the higher layers of the protocol stack, which is necessary
for the seamless integration of sensor nodes into the Internet. This is a difficult task
for several reasons, the most important are given below:
Sensor networks may consist of a very large number of nodes. This would result in
obvious problems as today there is a scarce availability of IP addresses. The largest
physical layer packet in IEEE 802.15.4 has 127 bytes; the resulting maximum frame
size at the media access control layer is 102 octets, which may further decrease based
on the link layer security algorithm utilized. Such sizes are too small when compared
to typical IP packet sizes.In many scenarios sensor nodes spend a large part of their
7
time in a sleep mode to save energy and cannot communicate during these periods.
This is absolutely anomalous for IP networks.
In other words, between two different objects communicating, the communication
path may be broken into different sections [18-19]. And how will all these products
manage to talk to each other? The 'language' will be based on a type of protocol, simi-
lar to the built-in formula that enables our mobile phones to talk using WiFi, ISA100,
ZigBee or BlueTooth[20]. Hence such an intelligent, configurable network interface
is an effective solution. A reconfigurable NIC (Network Interface Card) allows rapid
prototyping of new system architectures for network interfaces [21].
The architectures can be verified in real environment, and potential implementation
bottlenecks can be identified. Thus, what is needed is a platform, which combines the
performance and efficiency of special-purpose hardware with the versatility of a pro-
grammable device. Hence we have proposed the platform which is processor-based
implemented using a configurable hardware [22]. An FPGA (Field Programmable
gate array) with an embedded processor is a natural fit with this requirement. Also,
the reconfigurable NIC must have different memory interfaces including high capaci-
ty memory and high speed memory for adding new networking services. This is
SGMII interface (Fig. 2.) to support the various speed over Ethernet and Physical
media.
3.2 Scalability for nodes with ‘Kaivalyam’ packet.
The ‗Kaivalyam‘ packet is optimized for the smaller size of 256- bits due to limited
resources on node. The design is such that the 128-bits of IPv6 address are allocated
for Things unique universal address. It is desirable to program the node for smart
features which could be exercise through proper control register. The status of the
Thing could be read through the status register for sleeping, hibernation etc. for ener-
gy minimization of node. Here small overhead of 16-bit is kept for the parity infor-
mation with proper hamming distance windows for data error checking. The remain-
ing 96-bits corresponding to 12-bits of data payloads are kept for the node. Such small
size packets are very much possible on MtM sensors nodes.
The unique universal ID‘s, data, status and parity information of the node transmit-
ted over like RFID, , CAN, SPI, Zigbee, Bluetooth, WiFi, Wireless etc.; could be
extracted over front-end interfaces during the segregation with help of configurable
computing node having integrated N x 4 switch. Thus one can generate the 256-bits of
‗Kaivalyam‘ packets as shown in Figure 3. Data-synchronization for offline support
feature can be program in the soft-core processor (NIOS) built on SoC using FPGAs
to store the data of the devices over banks in the limited Flash memory ( or may be
over SAN‘s) in the offline mode and the same data can be transmitted during active
mode. Also the optional integration of software agents feature can be programmed in
the scenarios like complex global supply networks requiring more decentralized and
automated decision making. Software-agents have been researched broadly.
The Figure 4, shows a simplified high-level block diagram of the Triple Speed
Ethernet (TSE) design [23]. The design integrates two Altera TSE MegaCore func-
tions (MAC + PCS + PMA). The design uses the Stratix II GX PCI Express Devel-
opment Kit as a hardware platform, which includes two SFP (Small Form Pluggable)
cages. This design interfaces the TSE MegaCore function with a Copper or Optical
Fibre SFP module via a 1.25 Gbps serial transceiver that enables all 10, 100, and 1000
Mbps Ethernet operations. The design sends stream of Ethernet packets to the TSE
MegaCore function. The TSE MegaCore function [24] in turn sends out those packets
to the SFP modules (which serve the purpose of SGMII interface) connected to physi-
cal media here a optical fiber where the Ethernet packets are looped back externally
via SFP modules with an Ethernet cable assembly or through an Ethernet switch. The
design can demonstrate the operation of the TSE MegaCore function in various
modes with live traffic upto the maximum throughput rate and show the error rate in
the receiver, if any.
Fig. 1. The system details of the ‗Kaivalyam‘ Architecture of IoT.
Front-end For
Data Acquisition
System for
RFID,CAN, SPI,
Wireless,
Zigbee, Blue-
tooth, WiFi
nodes of Things.
Computing node
for segregating
front-end data
packets over
protocols to
‗Kaivalyam‘
packet
NIC
(TSE/WiFi)
LDPC encoding
BER Calculation
Calculation
ER Calculation
NIC
(TSE/WiFi)
LDPC decod-
ing
Demodulation
AWGN
Modulation
Front-end For
Data Acquisition
System for
RFID, CAN,
SPI, Wireless,
Zigbee, Blue-
tooth, WiFi
nodes of Things.
Computing node
for segregating
front-end data
packets over
protocols to
‗Kaivalyam‘
packet
9
3.3 LDPC Codes for Reliability of the Data.
An LDPC (Low Density Parity Check) code is a linear block code characterized by
a very sparse parity-check matrix. This means that the parity check matrix has a very
low concentration of 1‘s in it, hence the name ―low-density parity-check‖ code. The
sparseness of LDPC codes is what has interested researchers, as it can lead to excel-
lent performance in terms of bit error rates.
Fig. 2. Computing node for segregating data packets over protocols.
Table 1. Protocols of popular physical communication interfaces exploited by communication-
enabled objects.
Physical Com-
munication
interface type
Com-
munica-
tion type
Protocols
OSI Layers
Zigbee,Bluetooth,
RFID,etc.
Wire-
less
NWK/APS/API defined
by each standardization
body
Net-
work/Transport/Upper
WiFi
Wire-
less
IP/TCP-UDP
Network
/Transport/Upper
UWB
Wire-
less
Network /Transport
/Upper
Sensor network
busses (CAN, Profi-
bus)
Fixed
Upto Data link
Data link
Serial
Fixed
Upto Data link
Data link
USB
Fixed,
Wireless
Upto Data link
Data link
DeviceNet
Fixed
DeviceNet network
and transport
Network /Transport
Upper
ControlNet
Fixed
ControlNet network
and transport
Network /Transport
/Upper
Compu-
ting Node
with N x 4
switch
Standard Giga-
bit Media Inde-
pendent Interface
(SGMII/RGMII)
to Physical Media
with help of SFP‘s
Ethernet
Packet genera-
tor & monitor
Ethernet
Packet genera-
tor & monitor
Fig. 3. 256-bits of ‗Kaivalyam‘ packet datagram details for ‗Thing‘ on IoT.
Fig. 4. Simplified block diagram of Triple Speed Ethernet (TSE) reference design.
LDPC Codes are characterized by the sparseness of ones in the parity-check matrix.
This low number of one‘s allows for a large minimum distance of the code, resulting
in improved performance. Although proposed in the early 1960‘s by Gallager, it has
not been since recently that codes have emerged as a promising area of research in
achieving channel capacity. This is part due to the large amount of processing power
required to simulate the code. In the case of any coding scheme larger block length
codes provide better performance, but require more computing power. Performance of
a code is measured through its bit error rate (BER) vs. signal to noise ratio Eb/No in
dB. The curve of a good code will show a dramatic drop in BER as SNR improves.
The best codes have a cliff drop at an SNR slightly higher than the Shannon‘s limit
(0.18dB).In addition to presenting his seminal work in 1960, Gallager also provided a
Ethernet/IP
Fixed
IP/TCP-UDP
Network /Transport
/Upper
Power line (KNX,
LonWorks)
Fixed
Network /transport
layers according Net-
work layer/Transport to
KNX and Lonworks
specifications
Network
/Transport/Upper
128-bit ad-
dress parity of
things(IPv6)
96-bits
data
16-bit
status
16-bit Control
Register
16-bit Parity
NIOS
Soft-core
processor as
computing
node
SFP
Transceiver
A
LDPC -A
Coder
Decoder
TSE - A
Rx
Tx
TSE-B
Rx
Tx
LDPC -B
Coder
Decoder
SFP
Transceiver
B
11
decoding algorithm that is effectively optimal. The algorithm iteratively computes the
distributions of variables in graph-based models and comes under different names,
such as ‗Message Passing Algorithm‖, ―Sum-Product Algorithm (SPA)‖ or ―belief
propagation Algorithm‖. SPA-Logdomain and SPA-Min Sum Algorithm are the sim-
plified algorithms and easy to implement in an embedded environment[25] .
4 Results and Discussion
We have conceptualized the computing node using the NIOS-II soft-core processor
as a processing element having 8-bit interface with 4x4 I/O switch (Figure 2). The
design is implemented with ALTERA Inc. Quartus-II software (Ver. 8.0). The com-
puting node is a switch embedded with soft-core IP NIOS-II processor having pro-
cessing capabilities. Presently the features of generating and receiving ATM packets
programs are tested on the NIOS IDE of Quartus-II environment.
Table 2: Details of Input packets
Input data byte stream
1st
byte
2nd
byte
3rd
byte
4th
byte
5th byte
6-53 bytes
72
75
6C
95
76
11
A9
A5
BE
DC
AD
22
DF
EB
9E
02
E3
33
15
10
20
48
1A
44
Table 3.: Details of Output packets
Output data byte stream
1st
byte
2nd
byte
3rd
byte
4th
byte
5th byte
6-53 bytes
72
72
72
72
72
72
A9
A9
A9
A9
A9
A9
DF
DF
DF
DF
DF
DF
15
15
15
15
15
15
Functional simulations were performed using QUARTUS II software. Here the
computing node use the router static look-up table Virtual Channel Identifier (VCI)
information obtained for the shortest path optimization computation. This VCI infor-
mation can be altered for the Nx4 switch in the proposed ‗Kaivalyam‘ Architecture
to route the Kaivalyam packets on to duplex mode Ethernet Packets Generator and
Monitor for transmission over Ethernet backbone. Hence , depending on the look-up
table information the packets VCI information is altered for the specific route. The
functional simulation results are demonstrated in the input ‗Table 2‘ and output ‗Ta-
ble 3‘ for routing of the packets over 4x4 I/O port switch. The scheduler circuit in the
switch schedules the packets available in the ‗VOQ_input_x‘to the appropriate output
through crossbar fabric(Voq_fabric). The scheduling of the packets is implemented
using combination of priority based round robin diagonal propagation over the five
I/P ports and four O/P ports of the computing node design. Diagonal propagation has
advantage of dependencies over crossover fabric.
Fig. 5. Computing Node embedded with 4x4 I/O switch.
Simulation studies were performed (using MATLAB Simulink) for block length 512
bits, which is the minimum Ethernet frame length of 64 bytes. AWGN(Additive
White Gaussian Noise) is introduced in the channel and BER(Bit Error Rate) is com-
puted for different SNR(Signal to Noise Ratio). The SNR is increased from 1dB to
6dB. Figures 6(a) to 6(f) indicate that a BER of 10-4 to 10-5 can be achieved for SNR
of 2.5 dB. Also increasing the number of iterations decreases the BER. However for
considerable improvement in BER, you need to increase the block length. Also SPA-
Logdomain Algorithm is showing better performance than SPA-Min Sum Algorithm.
LDPC can be used for LANs since the normal noise level involved in optic fiber ca-
bles would range from 3 to 4 dB.
Concept that IoT has primarily to be focused on the ‗‗Things‖ and that the road to its
full deployment has to start from the augmentation in the Things‘ intelligence. This is
possible through proposed ‗Kaivalyam‘ packets which is in line of ‗spime‘. The spime
are defined as object that can be tracked through space and time throughout its life-
time and that will be sustainable, enhanceable, and uniquely identifiable [26]. Alt-
hough quite theoretical, the spime definition finds some real-world implementations
in so called Smart Items. These are a sort of sensors not only equipped with usual
wireless communication, memory, and elaboration capabilities, but also with new
potentials.
13
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6
10-6
10-5
10-4
10-3
10-2
10-1
100
Eb/No(dB)
BER
1 iter
2 iter
3 iter
4 iter
5 iter
6 iter
7 iter
8 iter
9 iter
10 iter
ldpc code(1024,512)
logdomain
frames =100
1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3
10-5
10-4
10-3
10-2
10-1
Eb/No(dB)
BER
11iter
12 iter
13 iter
14 iter
15 iter
16 iter
17 iter
18 iter
19 iter
20 iter
ldpc code(1024,512)
logdomain
frames =100
Fig.7a Fig.7b
1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3
10-5
10-4
10-3
10-2
10-1
Eb/No(dB)
BER
21 iter
22 iter
23 iter
24 iter
25 iter
26 iter
27 iter
28 iter
29 iter
30 iter
ldpc code(1024,512)
logdomain
frames =100
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6
10-6
10-5
10-4
10-3
10-2
10-1
100
Eb/No(dB)
BER
1 iter
2 iter
3 iter
4 iter
5 iter
6 iter
7 iter
8 iter
9 iter
10 iter
ldpc code(1024,512)
logdomainSim ple
frames =100
Fig.7c Fig.7d
1 1.5 2 2.5 3 3.5
10-6
10-5
10-4
10-3
10-2
10-1
100
Eb/No(dB)
BER
11 iter
12 iter
13 iter
14 iter
15 iter
16 iter
17 iter
18 iter
19 iter
20 iter
ldpc code(1024,512)
logdomainSim ple
frames =100
1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3
10-4
10-3
10-2
10-1
100
Eb/No(dB)
BER
21 iter
22 iter
23 iter
24 iter
25 iter
26 iter
27 iter
28 iter
29 iter
30 iter
ldpc code(1024,512)
logdomainSim ple
frames =100
Fig.7e Fig.7f
Fig. 6. BER vs SNR for 512 bytes block size for 100 frame over 1 to 30 iteration with logdo-
man(a-c) and logdomainSimple(d-f) algorithms.
Inputs are particularly expected from the Machine-to-Machine Workgroup of the
European Telecommunications Standards Institute (ETSI) and from some Internet
Engineering Task Force (IETF) Working Groups. 6LoWPAN [27], aiming at making
the IPv6 protocol compatible with low capacity devices, and ROLL [28], more inter-
ested in the routing issue for Internet of the Future scenarios, are the best candidates.
The multi sensor fusion algorithms like estimation (Non recursive, Recursive) classi-
fication (parametric, cluster, K-means), inference (Baysian, Dempster-shafer) and
ANN (Expert, Adaptive, Fuzzy) methods [29] integrated in the MtM could give diver
scope and potential application in the areas of co-operative, community sensing appli-
cations for IoT in areas of self-assembly and self-organization system. IEEE 802.11 is
a set of media access control (MAC) and physical layer (PHY) specifications for im-
plementing wireless local area network (WLAN) computer communication in the
2.4, 3.6, 5 and 60 GHz frequency bands. The IEEE 802.11 can be extended for higher
data rates with the multiple-antenna also known as spatial multiplexing with multiple
input multiple-output (MIMO) system design, wherein data for transmission is divid-
ed into independent data streams to be transmitted through multiple antennas. In a
multi-antenna system the adjacent antennas must be separated by a minimum dis-
tance, around half a wavelength (27 mm for 802.11ac), to reduce the coupling be-
tween antennas as well as correlation between streams. For applications where size
matters, this requirement limits the number of antennas and consequently the number
of streams and maximum bit rate. At 60 GHz the carrier wavelength is only 5 mm, so
relatively high gain antennas can be implemented in a small package with MtM tech-
nology in place. For example, a 13 dB patch array antenna printed on Duroid sub-
strate (r = 2.2) occupies an area of 5 mm × 6 mm [30].
On web front, we moved from www (static pages web) to web2 (social networking
web) to web3 (ubiquitous computing web), the need for data-on-demand using so-
phisticated intuitive queries increases significantly. Another interesting paradigm
which is emerging in the Internet of the Future context is the so called Web Squared,
which is an evolution of the Web 2.0. It is aimed at integrating web and sensing tech-
nologies [31] together so as to enrich the content provided to users. Presently, this is
obtained by taking into account the information about the user context collected by
the sensors (microphone, cameras, GPS, etc.) deployed in the user terminals. Such
Web Squared could be enhances for functionalities using more nodes for better virtu-
alization applications running over the IoT. Anyway, proprietary industrial approach-
es ignoring international standardization approaches as well as political discussion
will try to set their own de-facto-standards. A recent malware attack (Stuxnet), aiming
to spy on and reprogram Supervisory Control And Data Acquisition (SCADA) sys-
tems, has revealed once more the need for security in a future IoT. The Internet has
been misused to manipulate the virtual world, such as stock markets; and hence IoT
will have direct implications on the physical world. Measures ensuring the architec-
ture‘s resilience to attacks, data authentication, access control and client privacy need
to be established[32].
Nevertheless, there are also certain threats and issues of governance, security, and
privacy that need to be considered. Open governance in an IoT remains an important
issue. However, it may be assumed that the ongoing discussions between different
regions and countries will lead to a federated structure in the longer term, similar to
the domain structures we know from the Internet today. Among the possible applica-
15
tions, we may distinguish between those either directly applicable or closer to our
current living habitudes and those futuristic, which we can only fancy of at the mo-
ment, since the technologies and/or our societies are not ready for their deployment.
Acknowledgment
Authors would like to acknowledge financial assistance from University Grant
Commission (UGC, New Delhi) and ALTERA Inc.USA for the support under Uni-
versity Program.
References
1. Carrithers, M. 2001. The Buddha: A Very Short Introduction. Oxford University Press.
2. David Krackhardt, Kathleen Carley; ‗A PCANS Model of Structure in Organizations‘;
Proceedings of the 1998 International Symposium on Command and Control Research and
Technology; California, June 1998.
3. Matthias Kranz , Paul Holleis, Albert Schmidt, ‗Embedded Interaction Interacting with the
Internet of Things, Internet of Things Track, IEEE Internet Computing 2010.
4. Gartner‘s hype cycle special report for 2011, Gartner Inc., 2012.
http://www.gartner.com/technology/research/hype-cycles/.
5. D. Giusto, A. Iera, G. Morabito, L. Atzori (Eds.), The Internet of Things, Springer, 2010.
ISBN: 978-1-4419-1673-0.
6. http://www.etsi.org.
7. Z. Shelby, ETSI M2M Standardization, March 16, 2009, http://zachshelby.org.
8. J. Hui, D. Culler, S. Chakrabarti, 6LoWPAN: Incorporating IEEE 802.15.4 Into the IP Ar-
chitecture – Internet Protocol for Smart Objects (IPSO) Alliance, White Paper #3, January
2009, http://www.ipso-alliance.org.
9. F. F. Fernandes, R. S. Gad, V. R. Gad, Shane Lobo, G. M. Naik, J. S. Parab; ‗Embedded
System Communication: A Control Platform for Ethernet –Enabled Systems‘; Circuit Cel-
lar , Issue 255, October 2011.
10. Jason Hill , Robert Szewczyk , Alec Woo , Seth Hollar , David Culler , Kristofer Pister,
System architecture directions for networked sensors, ACM SIGPLAN Notices, v.35 n.11,
p.93-104, Nov. 2000 [doi>10.1145/356989.356998].
11. Internet 3.0: The Internet of Things. © Analysys Mason Limited 2010.
12. A. Dunkels, J.P. Vasseur, IP for Smart Objects, Internet Protocol for Smart Objects (IPSO)
Alliance, White Paper #1, September 2008, <http://www.ipso-alliance.org>.
13. Mohsen Darianian, Martin Peter Michael, ‗Smart Home Mobile RFID-based Internet-Of-
Things Systems and Services‘ 2008 International Conference on Advanced Computer
Theory and Engineering.
14. Aberer K, Hauswirth M, Salehi A (2006) Global Sensor Networks, Technical report LSIR-
REPORT- 2006-001.
15. Shim Y, Kwon T, Choi Y (2007) SARIF: A novel framework for integrating wireless
sensors and RFID networks, IEEE Wirel Commun14: 50-56.
doi:10.1109/MWC.2007.4407227.
16. Sung J, Sánchez López T, Kim D (2007) The EPC Sensor Network for RFID and
WSN Integration Infrastructure. Pervasive Computing and Communications Workshops,
Fifth IEEE International Conference on Pervasive Computing and Communications
Workshops (PerComW'07).
17. http://ieee802.org/15.
18. Rob van Kranenburg et al,‖The Internet of Things‖, 1st Berlin Symposium on Internet
and Society, 2011.
19. Internet of Things in 2020 ROADMAP FOR THE FUTURE‖,European Commission In-
formation Society and media & European Technology Platform on Small Systems Inte-
gration, May 2008.
20. Harald Sundmaeker et al Vision and Challenges for Realising the Internet of Things Eu-
ropean Commission Information Society and media, March 2010.
21. Tinoosh Mohsenin ,Rice University,‖ Design and Evaluation of FPGA-Based Gigabit-
Ethernet/PCI Network Interface Card Thesis ; 2004.
22. V. R. Gad, R. S. Gad, G. M. Naik, ―Performance Analysis of Gigabit Ethernet Standard for
various physical media using Triple Speed Ethernet IP Core on FPGA‖, International
Conference on Network Communications held in Delhi,India on 25-27 May 2012, AIRCC
, ICCSEA & Springer and published in International Journal of Advances in Intelligent
Computing.
23. Triple Speed Ethernet Data Path Reference Design AN-483-June 2009 ver. 1.1 Altera Cor-
poration (2009).
24. Triple-Speed Ethernet MegaCore Function User Guide © December 2010 Altera Corpora-
tion Application Note 483‘(2010).
25. W. E. Ryan, "An introduction to LDPC codes," August 2003.
26. B. Sterling, Shaping Things – Mediawork Pamphlets, The MIT Press, 2005.
27. N. Kushalnagar, G. Montenegro, C. Schumacher, IPv6 Over Low- Power Wireless Person-
al Area Networks (6LoWPANs): Overview, Assumptions, Problem Statement, and Goals,
IETF RFC 4919, August 2007.
28. M. Weiser, The computer for the 21st century, ACM Mobile Computing and Communica-
tions Review 3 (3) (1999) 3–11.
29. IEEE Sensors Journal, Vol. 2. No. 2,April 2002.
30. B. Biglarbegian et al., ―Optimized Microstrip AntennaArrays for Emerging Millimeter-
Wave Wireless Applications,‖ IEEE Trans. Antennas Propagation, vol. 59, no. 5, 2011, pp.
1742–47.
31. T. O‘Reilly, J. Pahlka, The ‗Web Squared‘ Era, Forbes, September 2009.
32. Computer law & s e c u r i t y r e v i e w 24 2 6 ( 2 0 1 0 ) 2 3 – 3 0.
U. V. Rane, M.Sc. ;Associate Professor, Dept. of Com-
puter Science, D.M.‘s College of Arts, Science and com-
merce, Assagoa,Bardez, Goa, India. Completed M.Sc. in
Electronics from Goa university. He has 14 years of teaching
experience. He is a co-ordinator for BCA course He is a
17
Research student in the Department of Electronics ,Goa University, Goa, India. His
current research interest includes Computer Networks and embedded system
V. R. Gad, M.Sc., M.Phil. Ph. D.; Head, Dept. of Com-
puter Science, G. V. M.‘s G. G. P. R. College of Com. &
Eco, Ponda, Goa, India. Completed M.Sc. in Electronics
from Goa University in 1994 and 1996 respectively and ob-
tained M.Phil. in Electronics from Bharathidasan University,
Tiruchirapalli in 2008. She has 14 years of teaching experi-
ence. She has worked on the University Grants Commission
Minor Research Project ―Design and Development of Com-
puterised ID Card System‖. She is a Research student in the Department of Electron-
ics, Goa University, Goa, India. Her current research interest includes Computer Net-
works, Error Control Coding and embedded system
Dr. R. S. Gad, M.Sc., Ph.D.; Associate Professor,Dept.of
Electronics, Goa University, Goa, India. Associated with
ALTERA Inc. USA under the MOU with ALTERA Univer-
sity program. Attended summer training at CEDT IISc, Ban-
galore for two months from April 27, 1998. Dr. Gad, was a
winner in Mentor Graphics Design contest ‗ Design and
verification of LC3 processor‘ for year 2010 in India. He is
also recipient of the Indian National Science Academy Fel-
lowship for the year 2012-13.
Prof. G. M. Naik , M.Sc., Ph.D.; Professor & Head,
Dept. of Electronics, Goa University, Goa, India. Prof.
Gourish Naik obtained his Ph.D ( Physics) from Indian
Institute of Science, Bangalore (1987) and served the
institute as research associate in the areas Communica-
tion till 1993. Has co- authored two books on Embedded
Systems and Programming published by Springer (Hol-
land). Books: 1)J. S. Parab, G. M. Naik et al ; ―Explor-
ing C For Microcontrollers: A Hands On Approach‖
Publisher: Springer Verlag, 2008.2) J. S. Parab, G. M. Naik et al ; ―Practical Aspects
Of Embedded System Design Using Microcontrollers‖ Publisher: Springer Verlag,
2008.
