Energy Efficient protocol design in Wireless Sensor Networks – Contributions to make the Ubiquitous Platform Greener

s all hardware resources as components. For example, calling the getData() command on a sensor component will cause it to later signal a dataReady() event when the hardware interrupt fires. While many components are entirely software based, the combination of split-phase operations and tasks makes this distinction transparent to the programmer. In both cases an event signals that the encryption operation is complete. ADC, ClockC, UART, SlavePin and SpiByteFifo are example hardware abstraction components. TinyOS commands and events are very short, due to limited code space and a finite state machine style of decomposition. The rich event processing model means an event or command call path can traverse several components. The TinyOS component model allows us to easily change the target platform from mote hardware to simulation by only replacing a small number of low-level components. The event-driven execution model can be exploited for efficient eventdriven simulation, and the whole program compilation process can be re-targeted for the simulator‟s storage model and native instruction set. The static component memory model of TinyOS simplifies state management for these large collections. Setting the right level of simulation abstraction can accurately capture the behavior and interactions of TinyOS applications. Figure 1.3: TinyOS Structure (Consist of scheduler and graph of components) 2.3 TOSSIM: A Simulator for TinyOS Sensor Networks The Necessity of Network Simulation: The emergence of wireless sensor networks brought many open issues to network designers. Traditionally, the three main techniques for analyzing the performance of wired and wireless networks are analytical methods, computer simulation, and physical measurement. However, because of many constraints imposed on sensor networks, such as energy limitation, decentralized collaboration and fault tolerance, algorithms for sensor networks tend to be quite complex and usually defy analytical methods that have been proved to be fairly effective for traditional networks. Furthermore, few sensor networks have come into existence, for there are still many unsolved research problems, so measurement is virtually impossible. It appears that simulation is the only feasible approach to the quantitative analysis of sensor networks. The event-driven nature of sensor networks means that testing an individual mote is insufficient. Programs must be tested at scale and in complex and rich conditions to capture a wide range of interactions. Deploying hundreds of motes is a daunting task, the focus of work shifts from research to maintenance, which is time-consuming due to the failure rate of individual motes. A simulator can deal with these difficulties, by providing controlled, reproducible environments, by enabling access to tools such as debuggers, and by postponing deployment until code is well tested and algorithms are understood. TOSSIM: TOSSIM is a discrete event simulator for TinyOS sensor networks. Instead of compiling a TinyOS application for a mote, users can compile it into the TOSSIM framework, which runs on a PC. This allows users to debug, test, and analyze algorithms in a controlled and repeatable environment. As TOSSIM runs on a PC, users can examine their TinyOS code using debuggers and other development tools. TOSSIM‟s primary goal is to provide a high fidelity simulation of TinyOS applications. For this reason, it focuses on simulating TinyOS and its execution, rather than simulating the real world. While TOSSIM can be used to understand the causes of behavior observed in the real world, it does not capture all of them, and should not be used for absolute evaluations. Related Publication: Swarup Kumar Mitra, Ayon Chakraborty, Subhajit Mandal and M.K.Naskar, Simulation of Wireless Sensor Networks using TinyOS A Case Study, In the Proceedings of the National Conference on Modern Trends in Electrical Engineering, pages EC 23 EC 26, Hooghly, West Bengal, July 2009. 3 Data Gathering Schemes in WSNs Data gathering is by far one of the most important aspects of research considering energy efficiency in the routing protocols for wireless sensor networks. Wireless sensor networks have emerged as a ubiquitous platform recently, and issues regarding the efficiency of energy usage by these devices play a very important role. These devices are equipped with negligible or less amount of battery power to sustain for a long time. Not only that, in most of the scenarios, where these networks are deployed it is infeasible or impossible sometimes to replace the battery power of the sensor nodes. One of the most fundamental aspects for energy consumption in sensor nodes is communication, other than sensing and computation costs. Optimization of communication costs is thus essential, which is a direct consequence of betterment of routing techniques in this type of wireless networks. A major portion of my contribution in this project deals with designing data gathering schemes for wireless sensor networks and optimization of routing techniques, described below. The first in this queue was the HDS or “Hybrid Data Gathering Scheme”. Published in the International Conference of Distributed Computing and Internet Technology (ICDCIT‟10), this work is a novel approach in minimizing not only the communication / energy overhead but also guarantees a minimal energy-latency product. It also distributes the energy consumption by the nodes by rotating the leader node, so as to increase the uniformity of energy content in the nodes. The uniform distribution of energy content in the nodes also helps to lessen the chances of a black hole or a sinkhole problem. The HDS protocol is based on the hybrid combination of two algorithms, SHORT and LBERRA. The LBERRA scheme is used to subdivide the sensor field into predefined clusters, and SHORT is applied to form a binary tree spanning the nodes. There are two types of leader nodes: one for each cluster, forming the root of the tree and the other one is the „sink‟ communicating the gathered data to the Base Station. In each of the data gathering rounds the leader node is changed The second work was related to optimization of routing chain through heuristic techniques. Firstly, I applied Particle Swarm Optimization to create the most energy efficient paths for communication in the sensor field. Then, I investigated the use of Genetic Algorithms (hybridized with simulated annealing) in solving the same problem. In these works, I not only devised the algorithm for the minimum-energy path formation, but also coded it in nesC discussed in the earlier section. The implementation and simulation in nesC guarantees the hardware feasibility of the algorithm in sensor nodes. Packet loss rates were also studied with varying network topology and signal strengths in communication between particular sensor nodes. In all the cases, a standard background noise was considered. This work followed a series of publications including three international conferences and two international journals. An extension of this work was to create energy efficient data gathering trees. Most algorithms developed in literature used greedy algorithms to construct routing trees which in most of the cases did not result in near-optimal energy usage. “ROOT” or “ROuting through Optimized Trees” was an answer to this need. Related Publications: International Conference: 1. Ayon Chakraborty, Kaushik Chakraborty, Swarup Mitra and Mrinal Naskar, An Optimized Lifetime Enhancement Scheme for Data Gathering in Wireless Sensor Networks, in the proceedings of The Fifth IEEE Conference on Wireless Communication and Sensor Networks, WCSN'09 Allahabad, India, (December, 2009). 2. Ayon Chakraborty, Swarup Mitra and Mrinal Naskar, An Efficient Hybrid Data Gathering Scheme in Wireless Sensor Networks, in the proceedings of The Sixth International Conference on Distributed Computing and Internet Technology, ICDCIT'10, Bhubaneswar, India. (February, 2010). 3. Ayon Chakraborty, Swarup K. Mitra and M.K. Naskar, Energy Efficient Routing in Wireless Sensor Networks: A Genetic Approach, in the Proceedings of the International Conference on Computer Communications and Devices (ICCCD 2010), IIT Kharagpur (December, 2010) 4. Kaushik Chakraborty, Ayon Chakraborty, Swarup Mitra and Mrinal Naskar, ROOT: Energy Efficient Routing through Optimized Tree in Sensor Networks, in the proceedings of The International Conference on Computer Communications and Devices – ICCCD'10, Kharagpur, India. (December, 2010).