Mahmuda Naznin

Finding the Optimal Percentage of Cluster Heads from a New and Complete Mathematical Model on LEACH

Network lifetime is one of the important metrics that indicate the performance of a sensor network. Different techniques are used to elongate network lifetime. Among them, clustering is one of the popular techniques. LEACH (Low-Energy Adaptive Clustering Hierarchy) is one of the most widely cited clustering solutions due to its simplicity and effectiveness. LEACH has several parameters that can be tuned to get better performance. Percentage of cluster heads is one such important parameter which affects the network lifetime significantly. At present it is hard to find the optimum value for the percentage of cluster head parameter due to the absence of a complete mathematical model on LEACH. A complete mathematical model on LEACH can be used to tune other LEACH parameters in order to get better performance. In this paper, we formulate a new and complete mathematical model on LEACH. From this new mathematical model, we compute the value for the optimal percentage of cluster heads in order to increase the network lifetime. Simulation results verify both the correctness of our mathematical model and the effectiveness of computing the optimal percentage of cluster heads to increase the network lifetime.

Stable Sensor Network (SSN): A Dynamic Clustering Technique for Maximizing Stability in Wireless Sensor Networks

Stability is one of the major concerns in advancement of Wireless Sensor Networks (WSN). A number of applications of WSN require guaranteed sensing, coverage and connectivity throughout its operational period. Death of the first node might cause instability in the network. Therefore, all of the sensor nodes in the network must be alive to achieve the goal during that period. One of the major obstacles to ensure these phenomena is unbalanced energy consumption rate. Different techniques have already been proposed to improve energy consumption rate such as clustering, efficient routing, and data aggregation. However, most of them do not consider the balanced energy consumption rate which is required to improve network stability. In this paper, we present a novel technique, Stable Sensor Network (SSN) to achieve balanced energy consumption rate using dynamic clustering to guarantee stability in WSN. Our technique is based on LEACH (Low-Energy Adaptive Clustering Hierarchy), which is one of the most widely deployed simple and effective clustering solutions for WSN. We present three heuristics to increase the time before the death of first sensor node in the network. We devise the algorithm of SSN based on those heuristics and also formulate its complete mathematical model. We verify the efficiency of SSN and correctness of the mathematical model by simulation results. Our simulation results show that SSN significantly improves network stability period compared to LEACH and its best variant.

Energy-Efficient Multiple Targets Tracking Using Target Kinematics in Wireless Sensor Networks

Target tracking is one of the cardinal applications of a wireless sensor network. Tracking multiple targets is more challenging than tracking a single target in a wireless sensor network due to targets’ movement in different directions, targets’ speed variations and frequent connectivity failures of low powered sensor nodes. If all the low-powered sensor nodes are kept active in tracking multiple targets coming from different directions of the network, there is high probability of network failure due to wastage of power. It would be more realistic if the tracking area can be reduced so that the less number of sensor nodes will be active which ultimately saves the energy consumption. Tracking area can be reduced by using the target’s kinematics. There is almost no method to track multiple targets, based on targets’ kinematics. In our paper, we propose a distributed tracking method for tracking multiple targets considering targets’ kinematics. We incorporate sensor network properties which results into a novel and service priority based dynamic tracking framework. We simulate our method by a sensor network simulator OMNeT++ and empirical results state that our proposed methodology outperforms traditional tracking algorithms.

Orthogonal Drawings of Plane Graphs without Bends

In an orthogonal drawing of a plane graph G each vertex is drawn as a point and each edge is drawn as a sequence of vertical and horizontal line segments. A point at which the drawing of an edge changes its direction is called a bend. Every plane graph of the maximum degree at most four has an orthogonal drawing, but may need bends. A simple necessary and sufficient condition has not been known for a plane graph to have an orthogonal drawing without bends. In this paper we obtain a necessary and sufficient condition for a plane graph G of the maximum degree three to have an orthogonal drawing without bends. We also give a linear-time algorithm to find such a drawing of G if it exists.

Monitoring Cost Reduction in Sensor Networks using Proximity Queries

Event detection and notification is a common task in a Wireless Sensor Networks (WSN). Efficient data aggregation and the minimization of energy consumption are the great research challenges in WSN. In WSN, normally the events are detected by more than one node and it is more reliable if the consistent event information is received from more than one node. Therefore, aggregated event information is more important than individual event information for energy saving and reliability. Proximity queries can be used to reduce the complexity of data aggregation and energy consumption. This paper presents an efficient and scalable hybrid framework for processing spatial and temporal proximity queries in WSN which we call Spatial and Temporal Processing (STP). STP builds tree structure with less overhead and reduces the event propagation cost through proximity queries. STP reduces energy consumption by reducing the number of aggregator nodes, which ultimately increases the network life time. STP eliminates the unnecessary aggregation of events using a tunable temporal proximity threshold. We compare STP’s performance with another spatial query processing method and we show that STP performs better.

ZDG: Energy efficient zone based data gathering in a wireless sensor network

In a WSN, it is desirable to keep many sensors active to collect data as much as possible which causes network failure, on the other hand, if most of the sensors are asleep, enough data can not be collected. Therefore, the trade-off between data collection and energy saving has made data gathering an interesting research field. To handle these challenges, we present an energy efficient data gathering method which increases the network lifetime and a good throughput. In our research, the tasks of data collection are distributed among the nodes of the different zones which resist the sensor nodes from going to the bottleneck state by minimizing latency. We design a load balanced data collection scheme by dividing the network zone into on-demand data collection clusters and routes which ultimately provides higher packet delivery. We find that our method provides longer network lifetime and higher throughput compared to some commonly used methods.

Shortening the Tour-Length of a Mobile Data Collector in the WSN by the Method of Linear Shortcut

In this work, we present a path-planning method for the Mobile Data Collector (MDC) in a wireless sensor network (WSN). In our method, a tour for the MDC is generated such that the latency of delivering data to the sink is reduced. We show that the TSP tour covers all the nodes of the WSN. However, the latency for the TSP tour may be prohibitively high for delay-sensitive real-time WSNs. We reduce the latency by shortening the given TSP tour using a method called Linear Shortcut. We observe that the MDC need not visit the exact location of the node. It only need to be in the proximity of the node as required by the transmission radius. We present an algorithm that iteratively shortens tour-length and hence, reduces the latency. We term the resulting tour as Tight Label Covering (TLC) tour. Experimental results show that TLC tour reduces latency by a significant margin.

STP: In-network aggregation through proximity queries in a Sensor Network

Event detection and notification is a common task in a Wireless Sensor Networks (WSN). Efficient data aggregation and minimization of energy consumption are the great research challenges in WSN. In WSN, aggregated event information is more important than individual event information for energy saving and reliability. Proximity queries or query approximation can be used to reduce the complexity of data aggregation and energy consumption. This paper presents an efficient and scalable hybrid framework for processing spatial and temporal proximity queries in WSN which we call STP. STP builds tree structure with less overhead, and reduces the event propagation cost through proximity queries. STP reduces energy consumption by reducing the number of aggregator nodes, which ultimately increases the network life time. STP eliminates the unnecessary aggregation of events using a tunable temporal proximity threshold. We compare STPs performance with another spatial query processing method and we show that, STP performs better.

Portable and secure multimedia data transfer in mobile phones using Record Management Store (RMS)

Nowadays mobile phones are expected to handle huge volume of multimedia data which is very challenging due to the small memory size of the mobile phone sets. We have developed an application that incorporates a novel technique which uses and enhances the Record Management Store (RMS), a subsystem of Mobile Information Device Profile (MIDP), which not only captures, upload and download multimedia data efficiently but also provides significant improvement over existing mobile applications in terms of portability and security. We illustrate our improvements by empirical analysys.

Optimal queries processing in a heterogeneous sensor network using multicommodity network flow method

In a wireless sensor network, it is a common practice to have in network partial data processing. As part of this, the mitigation of the demands issued by sink nodes in a heterogeneous resource constrained sensor network requires optimal resource sharing, which is a very challenging task. We find this task analogous to finding the optimal flow in a multi commodity flow network. The minimum cost multi commodity flow problem is an NP hard one. In our paper, we give an optimal cost algorithm to solve this problem where a special data structure is built, which we call Data Forwarding Tree. We compare our algorithm to a well known auction method where a commodity wins the bid based on the lowest cost path, and the other looser commodities are shipped using more expensive paths incurring more cost. An algorithmic analysis shows that our method is free of deadlock as well.