Optimal Number of Clusters in Dense Wireless Sensor Networks: A Cross- Layer Approach

Abstract-Cluster-based sensor networks have the advantages of reducing energy consumption and linkmaintenance cost. One fundamental issue in clusterbased sensor networks is to determine the optimal number of clusters. In this paper, we suggest a physical (PHY)/medium access control (MAC)/network (NET) cross-layer analytical approach to determine the optimal number of clusters with an objective of minimizing energy consumption in a high density sensor network. Our cross-layer design can incorporate many effects, including lognormal shadowing and two-slope path loss model in the physical layer, various MAC scheduling and multi-hop routing schemes. Compared with the base-line case with one cluster per observation area, a sensor network with the proposed optimal number of the clusters can reduce energy consumption by over 80% in some cases. We also verify by simulations that the analytical optimal cluster number can still function effectively regardless of different density of sensors in various observation areas

Dynamic deployment of applications in wireless sensor networks

Over the past decades, the progress inWirelss Sensor Network (WSN) technology, both in terms of processing capability and energy consumption reduction, has evolved WSNs into complex systems that can gather information about the monitored environment and make prompt and intelligent decisions. In the beginning, military applications drove the research and development of WSNs, with large-scale acoustic systems for underwater surveillance, radar systems for the collection of data on air targets, and Unattended Ground Sensor (UGS) systems for ground target detection. Typical civil WSNs are basically not complex monitoring systems, whose applications encompass environment and habitat monitoring, infrastructure security and terror threat alerts, industrial sensing for machine health monitoring, and traffic control. In these WSNs, sensors gather the required information, mostly according to a fixed temporal schedule, and send it to the sink, which interfaces with a server or a computer. Only at this point data from sensors can be processed, before being stored. Recent advances in Micro-Eletro-Mechanical Systems (MEMS), low power transceivers and microprocessor dimensions have led to cost effective tiny sensor devices that combine sensing with computation, storage and communication. These developments have contributed to the efforts on interfacing WSNs with other technologies, enabling them to be one of the pillars of the Internet of Things (IoT) paradigm. In this context, WSNs take a key role in application areas such as domotics, assisted living, e-health, enhanced learning automation and industrial manufacturing logistics, business/process management, and intelligent transportation of people and goods. In doing so, a horizontal ambient intelligent infrastructure is made possible, wherein the sensing, computing and communicating tasks can be completed using programmable middleware that enables quick deployment of different applications and services. One of the major issues with WSNs is the energy scarcity, due to the fact that sensors are mainly battery powered. In several cases, nodes are deployed in hostile or unpractical environments, such as underground or underwater, where replacing battery could be an unfeasible operation. Therefore, extending the network lifetime is a crucial concern. Lifetime improvement has been approached by many recent studies, from different points of view, including node deployment, routing schemes, and data aggregation Recently, with the consistent increase in WSN application complexity, the way distributed applications are deployed in WSNs is another important component that affects the network lifetime. For instance, incorrect execution of data processing in some nodes or the transmission of big amounts of data with low entropy in some nodes could heavily deplete battery energy without any benefit. Indeed, application tasks are usually assigned statically to WSN nodes, which is an approach in contrast with the dynamic nature of future WSNs, where nodes frequently join and leave the network and applications change over the time. This brings to issue talked in this thesis, which is defined as follows. Dynamic deployment of distributed applications in WSNs: given the requirements of WSN applications, mostly in terms of execution time and data processing, the optimal allocation of tasks among the nodes should be identified so as to reach the application target and to satisfy the requirements while optimizing the network performance in terms of network lifetime. This issue should be continuously addressed to dynamically adapt the system to changes in terms of application requirements and network topology

Energy conservation in wireless sensor networks

This dissertation presents a system-level approach for minimizing the power expended in achieving communication between a ground-based sensor network and an overhead Unmanned Aerial Vehicle (UAV). A subset of sensor nodes, termed a transmit cluster, aggregates data gathered by the network and forms a distributed antenna array, concentrating the radiated transmission into a beam aimed towards the UAV. We present a method for more uniformly distributing the energy burden across the sensor network, specifying the time that should elapse between reassignments of the transmit cluster and the number of hops that should be placed between successive transmit clusters. We analyze the performance of two strategies for reconfiguring the communication burden between the sensor network and the UAV in order to bring the UAV and the sensor network's beam into alignment quickly, while minimizing the energy expenditure. We analyze the optimal number of nodes that should participate in a beamforming process in order to minimize the energy expended by the network, and we provide a framework to analyze the minimum energy expended in a simple beamforming algorithm. Finally, we analyze the probability that an arbitrarily selected sensor node is connected to a specified number of other nodes and we present an algorithm for the formation of near-linear arrays given random placement of nodes.http://archive.org/details/energyconservati1094510228Approved for public release; distribution is unlimited

Visoko-pouzdan prenos podataka kod bežičnih senzorskih mreža sa malom potrošnjom energije primenom 2D-SEC-DED tehnike kodiranja

This dissertation deals with the challenges of energy efficiency in systems with limited resources of homogeneous and heterogeneous wireless sensory networks for data collection applications in real environmentals. This research covers several fields from physical layer optimization up to network layer solutions. The problem which has to be solved is viewed from three different perspectives: the energy profile of the nodes with a special emphasis on the activity of the sensing block, the network protocol with a special focus on finding an adequate coding technique that need to reduce or eliminate the request for retransmission and evaluating the range of transmission for the proposed encoding technique. If energy efficiency in wireless sensor networks is formulated as a load balancing problem then the power management unit can significantly contribute to reduction in power consumption. Power management is implemented by switching on/off individual subblocks of the sensor node independently of the hardware platform. By reducing energy consumption both an extension of the lifetime of the sensor node and sensor network, is achieved. The obtained energy profiles reveal significant differences in energy consumption of wireless sensor nodes depending in terms of external sensors number, resolution of the analog-to-digital converter, network traffic dynamics, topology of the network, applied coding techniques, operating modes and activities during the lifetime of the sensor node and other factors. In this sense, the application of combination of power aware techniques, such as the duty-cycling at system-level, and power gating at the level of sensor elements, i.e. sensors, is proposed. An evaluation of the approach shows that energy consumption reduction three orders of magnitude on average can be achieved, when these two techniques are incorporated into the sensor node. On the other hand, in the wireless sensor networks, the choice of coding scheme, i.e. channel coding depends on the application and characteristics-, model-, type-errors appearing in the wireless channel. For example, one encoding technique is preferred for use when burst errors patterns are dominant, while another coding technique is more acceptable in situations where noise causes random errors that are either single or double in nature. Bearing this in mind, along with the analysis of channel characteristics, in this dissertation, we propose a new massage coding technique by which on extend traditional protocols with aim to improve energy efficiency, while maintaining high reliability in data transmission and low latency of message transfer. Namely, channel evaluation in wireless sensor networks used in industry shows that most of the errors are of single or double nature, and burst type errors are present, but rarely. In this context, in this dissertation, an effective technique for correcting errors at a destination (FEC) based on Hamming's coding scheme of relatively low complexity, called Two Dimensional-Single Error Correction- Double Error Detection (2D-SEC-DED) was developed. The proposed encoding technique is intendet to minimize packet retransmissions, thus saving energy. Evaluation of the proposed encoding scheme shows that the code is able to correct all single errors and 99.99% of double/multiple errors. The analysis was carried out through the implementation, in MATLAB, of two versions of Rendezvous Protocol for Long Life (RPLL), called Modified RPLL (M-RPLL) and Ordinary RPLL (O-RPLL), respectively. The energy gain achieved in this way is used to improve the performance of wireless transmission, such as increasing of the transmission range. As illustration, for indoor environment characterized by the path loss exponent 4 at the target BER of 5 10 4 , the proposed encoding scheme is able to improve the transmission distance by about 18 m , or the received signal strength (RSSI) by about 8.5 dBm compared to wireless sensor networks with encoding schemes without possibility to correct errors