Analysis of energy complexity under mobility for cluster routing in Wireless Ad hoc Sensor Networks

Wireless Ad hoc Sensor Networks (WASNs) are self organizing, self-configuring and multi-hop wireless networks, in which each node communicates with other nodes directly or indirectly through intermediate nodes without any infrastructure. Besides these characteristics challenges like limited energy, dynamic topology, low bandwidth and security exists. The arrangement of nodes in WASN is called topology which changes dynamically with respect to time and mobility. Energy conservation is a major challenge because of the limited battery resource. In order to conserve energy in the network layer many techniques were used. These techniques can be classified mainly into two categories. The first technique is making small number of nodes awake to transfer bulk amount of data and to maintain the network connectivity and making the remaining nodes to be in sleep mode to conserve energy. This technique is effective for low traffic conditions. In the second technique, topology in the network is controlled by power control technique. This technique is effective for high data traffic. To conserve energy in an efficient manner, a Dynamic Traffic Energy Efficient Topology (DTEET) dynamically adjusts network topology for various network traffic conditions in WASN either in low traffic or high traffic is proposed. And the proposed protocol DTEET is stimulated by using AODV as routing protocol using network simulator ns2.34 and compared with AODV and DSR [1,2]. The simulation results revealed that the proposed scheme performs well in terms of energy, delay, delivery ratio and mobility

Energy related research in wireless ad hoc sensor networks (WASNs) is focusing on energy saving techniques in the application-, protocol-, service-, or hardware-level. Little has been done to manage the finite amount of energy for a given (possibly optimally-designed) set of applications, protocols and hardware. Given multiple candidate applications (i.e., distributed algorithms in a WASN) of different energy costs and different user rewards, how does one manage a finite energy amount? Where does one provide energy, so as to maximize the useful work done (i.e., maximize user rewards)? We formulate the problem at the node-level, by having system-level “hints” from the applications. In order to tackle the central problem we first identify the energy consumption patterns of applications in WASNs, we propose ways for real-time measurements of the energy consumption by individual applications, and we solve the problem of estimating the extra energy consumption that a new application brings to a set of executing applications. Having these tools at our disposal, and by properly abstracting the problem we present an optimal admission control policy and a post-admission policing mechanism at the node-level. The admission policy can achieve up to 48% increase in user rewards compared to the absence of energy management, for a variety of application mixes.

When using multiple sensor nodes in wireless sensor networks (WSNs) for monitoring (or measuring) parameters of the target and sending the result to the base station (BS), data redundancy is an inevitable problem. The measured data often contains the same information, and sending redundant data to BS causes the waste of energy of sensor nodes and the risk of congestion. Multi-sensor data fusion in WSNs is a technology of gathering and processing data applied from node to BS. It improves the performance of surveillance systems by allowing the obtained sensed information from multiple sensor nodes aggregated to one unified format data packet to send to BS to make decision. In this paper, we propose a solution namely DF-SWin for sampling sensor node in the cluster to optimize the energy conservation of sensor nodes in clusters and cluster head.

Advanced transportation sensors use a wireless medium to communicate and use data fusion techniques to provide complete information. Large-scale use of intelligent transportation sensors can lead to data bottlenecks in an ad-hoc wireless sensor network, which needs to be reliable and should provide a framework to sensors that constantly join and leave the network. A possible solution is to use public transportation vehicles as data fusion nodes or cluster heads. This paper presents a mathematical programming approach to use public transportation vehicles as cluster heads. The mathematical programming solution seeks to maximize benefit achieved by covering both mobile and stationary sensors, while considering cost/penalty associated with changing cluster head locations. A simulation is developed to capture realistic considerations of a transportation network. This simulation is used to validate the solution provided by the mathematical model.

Wireless ad-hoc sensor networks will provide one of the missing connections between the Internet and the physical world. One of the fundamental problems in sensor networks is the calculation of coverage. Exposure is directly related to coverage in that it is a measure of how well an object, moving on an arbitrary path, can be observed by the sensor network over a period of time.In addition to the informal definition, we formally define exposure and study its properties. We have developed an efficient and effective algorithm for exposure calculation in sensor networks, specifically for finding minimal exposure paths. The minimal exposure path provides valuable information about the worst case exposure-based coverage in sensor networks. The algorithm works for any given distribution of sensors, sensor and intensity models, and characteristics of the network. It provides an unbounded level of accuracy as a function of run time and storage. We provide an extensive collection of experimental results and study the scaling behavior of exposure and the proposed algorithm for its calculation.

The measurement of CTD is the key of oceanic research. The conventional methods have lots of limitations. Ad Hoc wireless sensor network is built from sensor nodes that may spontaneously create, assemble themselves, dynamically adapt to manage movement of sensor nodes, which is most suitable to the CTD measurement. The positions of CTD sensors detected by GPS are changed by waves, which leads to the changes of network topology and may increase the energy consumption. Two steps are taken to enhance the life of the network. First is the adoption of energy efficient routing protocols based on rCAMP. Second is the data compression. Then data are transferred to control center and processed by B-Spline algorithm to build the ocean CTD model.

This paper proposes a distributed routing protocol for ad hoc sensor networks which uses fuzzy logic. Each sensor uses a fuzzy decision making process to find the best cluster head. Simulation shows that this protocol is able to dynamically adapt to sensorspsila mobility and failure. By a new load balancing method, it provides also stable clusters and so a cluster head have greater lifetime, which results minimum message exchange and so minimum energy consumption.

Topology control is an important problem in wireless ad-hoc sensor networks. The aim of the topology control is to maintain desired properties of the network topology to improve the performance of networking algorithms (e.g. routing). In this paper, we present a distributed algorithm for assigning minimum possible power to all the nodes in the wireless sensor networks, such that the network is K-connected. Extensive simulation has been performed to prove the optimality of the algorithm

From the perspective of media protocol control and routing of directional wireless ad hoc sensors networks, neighbor discovery protocol is an important problem to be solved first. In the past period of time, some methods have been studied on neighbor discovery protocol, but they have a common defect of link collision. The collision is caused by mutual interference of multiple transmitting nodes which are in one reception beam of the receiving node. To solve this problem, we propose a neighbor discovery algorithm using a bi-directional carrier sense collision avoidance and multi subchannels based on a scan-based algorithm (BD-SBA). Based on a scan-based algorithm (SBA), bi-directional carrier sense of the BD-SBA algorithm is performed in the first broadcast step which can reduce the collision of broadcasting the scanning request (SREQ) frames. In the second step (the reply step), the mechanism of multiple subchannels and multiple slots is applied to reduce the collision of the scanning response (SRES) frames. From the analysis and simulation, we can see that nodes using proposed algorithm can discover their neighbor nodes in fewer time. Moreover, the proposed algorithm has better performance for different beamwidths and densely distributed scenes. So it has great significance in engineering application.

This paper develops an iterative algorithm for estimating the least-cost paths that can lead from a given initial state to a given final state in a wireless ad hoc sensor network (WASN) that is modeled by a Petri net. In particular, a WASN can be assumed as a multi-hop network and its topology can be modeled as a Petri net. We assume that each link between two nodes (each transition in the net) is associated with a positive cost to capture its likelihood (e.g., in terms of the transmission time, distance between nodes, or the link capacity). Given the structure of the Petri net and its initial/final state, we aim at finding the paths (with length less than or equal to a given bound L) that could lead us from the initial state to the final state while having the least total cost. We develop an iterative algorithm that finds the least-cost paths with complexity that is polynomial in the length L.

The paper proposes a new clustering approach based on genetic algorithms (GA) and devoted to improving energy efficiency in ad-hoc wireless sensor networks (WSN). A special designed MATLAB framework operates as test bench to evaluate different implementations. The solutions of the optimization algorithms provide the number of clusters along with the cluster structures. Real-time implementations of such algorithms justify the necessity to minimize their execution time. We have devised custom genetic operators in order to improve the GA convergence. The process of fine tuning of the GA parameters proved to be also extremely important. Intensive simulation studies have confirmed the validity and efficiency of the proposed solutions. Using our approach, we have improved the speed of convergence for the GA up to 50%, as compared with existing approaches, while reducing considerably the minimum communication distance in the ad-hoc WSN.

Early detection of a forest fire can save our flora and fauna. Ad Hoc Wireless Sensor Networks (WSN) plays an important role in detecting forest fire. This article proposes a model for early detection of forest fire through predictive analytics. In this approach, the forest area is divided into different zones. Status of a zone, i.e., High Active (HA), Medium Active (MA), and Low Active (LA), is predicted by applying the semi-supervised classification technique. Each zone has static sensors, mobile sensors, and an Initiator node. Initiator nodes of LA and MA zone transfer their mobile nodes (MN) to the nearer HA zone for the quick prediction of forest fire by using the Random trajectory generation (RTG) technique. This technique generates the intermediate points between LA/MA to HA zone to create the movement path of MN. Compressed sensing based Gradient descent (GD) localization technique is used to track the movement of MN by the anchor nodes. This technique reduces the energy consumption of MN that causes an increase in network lifetime. The analysis of the localization error of MN during its traveling towards the HA zone increases the accuracy of its path detection. Thus the increase of sensor nodes in the HA zone results in transferring a huge amount of data from HA zone to base station for quick prediction of a forest fire.

Recursive least-squares (RLS) schemes are of paramount importance for online estimation and tracking of signals, especially when the state and/or data model are unknown. Here, a distributed RLS-like algorithm is developed that can operate in ad hoc wireless sensor networks (WSNs). The novel algorithm is obtained by writing the weighted squared-error cost associated with an RLS algorithm in a separable form and applying the alternating-direction method of multipliers to minimize it in a distributed fashion. This distributed adaptive scheme can be applied in general WSNs that are challenged by communication noise and do not necessarily possess a Hamiltonian cycle. Relative to competing alternatives, the novel algorithm offers more efficient communications. Numerical examples indicate that the proposed scheme is resilient to communication noise, while it performs efficient tracking of time-varying processes.

Coverage is a typical problem in wireless sensor networks to fulfil the issued sensing tasks. In general, sensing coverage represents how well an area is monitored by sensors. The quality of a sensor network can be reflected by the levels of coverage and connectivity that it offers. The coverage problem has been studied extensively, especially when combined with connectivity and energy efficiency. Constructing a connected fully covered, and energy efficient sensor network is valuable for real world applications due to the limited resources of sensor nodes. In this paper, we survey recent contributions addressing energyefficient coverage problems in the context of static WASNs, networks in which sensor nodes do not move once they are deployed and present in some detail of the algorithms, assumptions, and results. A comprehensive comparison among these approaches is given from the perspective of design objectives, assumptions, algorithm attributes and related results.

Wireless ad-hoc sensor networks have caught the fancy of many researchers through the world in a very small span of time. Although notable progress has been made in several key areas, several researchers falter in the way they look at sensor networks i.e. just another kind of wireless ad hoc networks, albeit one composed of energy constrained nodes. A key aspect that makes sensor networks different from traditional networks is their strong link to the physical world. We develop this perspective by studying an interesting class of sensor network applications called aggregation applications.We argue that when a user queries for aggregates like maximum, average etc., he is interested in knowing the statistics of the underlying physical process. In general, this is not equivalent to the statistics gathered from a bunch of nodes. We introduce a distinction between aggregates calculated over a region and aggregates calculated over a discrete set of nodes. We classify them as spatial and nodal aggregates respectively. Till now, researchers have followed the approach of doing nodal aggregation in response to every user query in sensor networks. For a continuous physical process and a random deployment of sensor nodes, which are a norm for sensor networks, the conventional approach of doing nodal aggregation produces inaccurate results.We verify our claim by studying a specific aggregation function namely the average over a region (spatial average) in detail. We use a voronoi-based approach to propose an algorithm for calculating spatial average in sensor networks. The algorithm can work in two modes -- centralized and distributed. The energy and accuracy tradeoff associated with each of the two modes is analyzed in detail. The efficacy of the proposed algorithm is tested over a wide variety of physical processes including real precipitation data of South America acquired over a span of past 50 years. We will show that our algorithm gives a 2-8 times performance gain as compared to the conventional approach of doing nodal aggregation. We further explore the robustness of our algorithm to link failures and channel impairments.Although not 100% accurate, our approach is a simple, efficient and a practical solution for calculating the spatial average. We have implemented our algorithm on Berkeley motes. Our first prototype implementation occupies less than 12K flash ROM and consumes less than 1.6K run-time memory. Our algorithm can be easily integrated with available frameworks for doing aggregation in sensor networks. For a minimal cost, our algorithm can be used to provide a spatial aggregation service on a network of motes.