Adjacency Matrix Based Energy Efficient Scheduling using S-MAC Protocol in Wireless Sensor Networks

Communication is the main motive in any Networks whether it is Wireless Sensor Network, Ad-Hoc networks, Mobile Networks, Wired Networks, Local Area Network, Metropolitan Area Network, Wireless Area Network etc, hence it must be energy efficient. The main parameters for energy efficient communication are maximizing network lifetime, saving energy at the different nodes, sending the packets in minimum time delay, higher throughput etc. This paper focuses mainly on the energy efficient communication with the help of Adjacency Matrix in the Wireless Sensor Networks. The energy efficient scheduling can be done by putting the idle node in to sleep node so energy at the idle node can be saved. The proposed model in this paper first forms the adjacency matrix and broadcasts the information about the total number of existing nodes with depths to the other nodes in the same cluster from controller node. When every node receives the node information about the other nodes for same cluster they communicate based on the shortest depths and schedules the idle node in to sleep mode for a specific time threshold so energy at the idle nodes can be saved.Comment: 20 pages, 2 figures, 14 tables, 5 equations, International Journal of Computer Networks & Communications (IJCNC),March 2012, Volume 4, No. 2, March 201

ENERGY EFFICIENT SLEEP WAKEUP SCHEDULING METHOD FOR P-COVERAGE AND Q-CONNECTIVITY MODEL IN TARGET BASED WIRELESS SENSOR NETWORKS

Energy limitations are the problem that gets the most attention in the term of Wireless Sensor Networks (WSN). Sleep wakeup scheduling method is one of the most efficient techniques to increase sensor node operational time on WSN. However, in the target-based WSN environment with p-coverage and q-connectivity models, the use of wake-up scheduling has to consider the constraints on the number of connectivity on the sensor and coverage on the target. Genetic Algorithm is a solution to the problem of sleep-wake scheduling with multi-objective problems. This study proposes a new method of sleep wakeup scheduling based on Genetic Algorithm for energy efficiency in target-based WSN with p-coverage and q-connectivity models. This new method uses the sensor range, connectivity range and energy as an objective function of the fitness function in the Genetic Algorithm. With the presence of energy as a factor of the objective function can increase energy efficiency in target-based WSN with p-coverage and q-connectivity models

Location Based Power Reduction Cloud Integrated Social Sensor Network

It is great to hear about the advancements in wireless sensor networks and their applications, as well as the integration of cloud computing to enhance data analysis and storage capabilities. Indeed, these technologies have opened up numerous possibilities across various fields, including infrastructure tracking, environmental monitoring, healthcare, and more. The concept of a social sensor cloud, as you mentioned, brings an interesting dimension to this technology landscape by focusing on knowledge-sharing and connecting like-minded individuals or organizations. This could potentially lead to more collaborative and efficient solutions across a wide range of domains. Energy efficiency is a critical consideration in the design and operation of wireless sensor networks and the cloud infrastructure that supports them. The limited battery life of sensors necessitates careful management of energy consumption to ensure optimal functionality and longevity. Sleep scheduling methods are a common technique used to manage energy consumption in these networks. By coordinating when sensors are active and when they are in a low-power sleep mode, energy consumption can be significantly reduced without compromising the network's overall effectiveness. In the context of the Social Sensor Cloud, managing energy efficiency becomes even more crucial due to the shorter battery life of the sensors involved. This is particularly relevant given the growing concerns about environmental sustainability and the need to reduce energy consumption across technological systems. It's clear that your research paper addresses these challenges head-on, by exploring energy-efficient techniques for the Social Sensor Cloud. Sleep scheduling is just one of the many strategies that researchers and engineers are working on to strike a balance between functionality and energy consumption. Other methods might include optimizing data transfer protocols, developing energy-harvesting mechanisms, and enhancing sensor hardware efficiency. As technology continues to evolve, the integration of wireless sensor networks, cloud computing, and social networks will likely pave the way for innovative solutions and transformative applications. Addressing energy efficiency concerns will undoubtedly play a crucial role in ensuring the long-term viability and positive impact of these technologies

Energy efficient tdma sleep scheduling in wireless sensor networks

Abstract—Sleep scheduling is a widely used mechanism in wireless sensor networks (WSNs) to reduce the energy consumption since it can save the energy wastage caused by the idle listening state. In a traditional sleep scheduling, however, sensors have to start up numerous times in a period, and thus consume extra energy due to the state transitions. The objective of this paper is to design an energy efficient sleep scheduling for low data-rate WSNs, where sensors not only consume different amounts of energy in different states (transmit, receive, idle and sleep), but also consume energy for state transitions. We use TDMA as the MAC layer protocol, because it has the advantages of avoiding collisions, idle listening and overhearing. We first propose a novel interference-free TDMA sleep scheduling problem called contiguous link scheduling, which assigns sensors with consecutive time slots to reduce the frequency of state transitions. To tackle this problem, we then present efficient centralized and distributed algorithms that use time slots at most a constant factor of the optimum. The simulation studies corroborate the theoretical results, and show the efficiency of our proposed algorithms

Improving energy efficiency in wireless sensor networks through scheduling and routing

This paper is about the wireless sensor network in environmental monitoring applications. A Wireless Sensor Network consists of many sensor nodes and a base station. The number and type of sensor nodes and the design protocols for any wireless sensor network is application specific. The sensor data in this application may be light intensity, temperature, pressure, humidity and their variations .Clustering and routing are the two areas which are given more attention in this paper.Comment: 7 Pages, 2 Figures and 1 Tabl

Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverage aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

A Coverage Monitoring algorithm based on Learning Automata for Wireless Sensor Networks

To cover a set of targets with known locations within an area with limited or prohibited ground access using a wireless sensor network, one approach is to deploy the sensors remotely, from an aircraft. In this approach, the lack of precise sensor placement is compensated by redundant de-ployment of sensor nodes. This redundancy can also be used for extending the lifetime of the network, if a proper scheduling mechanism is available for scheduling the active and sleep times of sensor nodes in such a way that each node is in active mode only if it is required to. In this pa-per, we propose an efficient scheduling method based on learning automata and we called it LAML, in which each node is equipped with a learning automaton, which helps the node to select its proper state (active or sleep), at any given time. To study the performance of the proposed method, computer simulations are conducted. Results of these simulations show that the pro-posed scheduling method can better prolong the lifetime of the network in comparison to similar existing method

Energy-Optimal Scheduling in Low Duty Cycle Sensor Networks

Energy consumption of a wireless sensor node mainly depends on the amount of time the node spends in each of the high power active (e.g., transmit, receive) and low power sleep modes. It has been well established that in order to prolong node's lifetime the duty-cycle of the node should be low. However, low power sleep modes usually have low current draw but high energy cost while switching to the active mode with a higher current draw. In this work, we investigate a MaxWeightlike opportunistic sleep-active scheduling algorithm that takes into account time- varying channel and traffic conditions. We show that our algorithm is energy optimal in the sense that the proposed ESS algorithm can achieve an energy consumption which is arbitrarily close to the global minimum solution. Simulation studies are provided to confirm the theoretical results