Abstract: Since last decade, wireless sensor networks (WSNs) have been used in many areas like health care, agriculture, defense, military, disaster hit areas and so on. Wireless Sensor Networks consist of a Base Station (BS) and more number of wireless sensors in order to monitor temperature, pressure, motion in different environment conditions. The key parameter that plays a major role in designing a protocol for Wireless Sensor Networks is energy efficiency which is a scarcest resource of sensor nodes and it determines the lifetime of sensor nodes. Maximizing sensor node’s lifetime is an important issue in the design of applications and protocols for Wireless Sensor Networks. Clustering sensor nodes mechanism is an effective topology control approach for helping to achieve the goal of this research. In this paper, the researcher presents an energy efficiency protocol to prolong the network lifetime based on Energy efficient clustering algorithm. The Low Energy Adaptive Clustering Hierarchy (LEACH) is a routing protocol for clusters which is used to lower the energy consumption and also to improve the lifetime of the Wireless Sensor Networks. Maximizing energy dissipation and network lifetime are important matters in the design of applications and protocols for wireless sensor networks. Proposed system is to maximize the lifetime of the Wireless Sensor Networks by choosing the farthest cluster head (CH) instead of the closest CH and forming the cluster by considering the following parameter metrics such as Node’s density, residual-energy and distance between clusters (inter-cluster distance). In this paper, comparisons between the proposed protocol and comparative protocols in different scenarios have been done and the simulation results showed that the proposed protocol performs well over other comparative protocols in various scenarios.
Abstract: Wireless sensor networks have been used in wide
areas of application and become an attractive area for researchers in
recent years. Because of the limited energy storage capability of
sensor nodes, Energy consumption is one of the most challenging
aspects of these networks and different strategies and protocols deals
with this area. This paper presents general methods for designing low
power wireless sensor network. Different sources of energy
consumptions in these networks are discussed here and techniques for
alleviating the consumption of energy are presented.
Abstract: A considerable amount of energy is consumed during
transmission and reception of messages in a wireless mesh network
(WMN). Reducing per-node transmission power would greatly
increase the network lifetime via power conservation in addition to
increasing the network capacity via better spatial bandwidth reuse. In
this work, the problem of topology control in a hybrid WMN of
heterogeneous wireless devices with varying maximum transmission
ranges is considered. A localized distributed topology control
algorithm is presented which calculates the optimal transmission
power so that (1) network connectivity is maintained (2) node
transmission power is reduced to cover only the nearest neighbours
(3) networks lifetime is extended. Simulations and analysis of results
are carried out in the NS-2 environment to demonstrate the
correctness and effectiveness of the proposed algorithm.
Abstract: In this research, we study a control method of a multivehicle
system while considering the limitation of communication
range for each vehicles. When we control networked vehicles with
limitation of communication range, it is important to control the
communication network structure of a multi-vehicle system in order
to keep the network-s connectivity. From this, we especially aim to
control the network structure to the target structure. We formulate
the networked multi-vehicle system with some disturbance and the
communication constraints as a hybrid dynamical system, and then
we study the optimal control problems of the system. It is shown
that the system converge to the objective network structure in finite
time when the system is controlled by the receding horizon method.
Additionally, the optimal control probrems are convertible into the
mixed integer problems and these problems are solvable by some
branch and bound algorithm.