X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/Sensornets15.git/blobdiff_plain/30623c88a2804ea8abf07dd5b15eeacdcd39a33d..8abf40a6144c2ae97b77ec718fef360e652d01b9:/Example.tex?ds=inline diff --git a/Example.tex b/Example.tex index cef7ad1..38cc14f 100644 --- a/Example.tex +++ b/Example.tex @@ -91,7 +91,7 @@ means of recharging or replacing, usually due to environmental (hostile or unpractical environments) or cost reasons. Therefore, it is desired that the WSNs are deployed with high densities so as to exploit the overlapping sensing regions of some sensor nodes to save energy by turning off some of them during -the sensing phase to prolong the network lifetime. \textcolor{blue}{A WSN can use various types of sensors such as \cite{ref17,ref19}: thermal, seismic, magnetic, visual, infrared, acoustic, and radar. These sensors are capable of observing different physical conditions such as: temperature, humidity, pressure, speed, direction, movement, light, soil makeup, noise levels, presence or absence of certain kinds of objects, and mechanical stress levels on attached objects. Consequently, a wide range of WSN applications such as~\cite{ref22}: health-care, environment, agriculture, public safety, military, transportation systems, and industry applications.} +the sensing phase to prolong the network lifetime. \textcolor{blue}{A WSN can use various types of sensors such as \cite{ref17,ref19}: thermal, seismic, magnetic, visual, infrared, acoustic, and radar. These sensors are capable of observing different physical conditions such as: temperature, humidity, pressure, speed, direction, movement, light, soil makeup, noise levels, presence or absence of certain kinds of objects, and mechanical stress levels on attached objects. Consequently, there is a wide range of WSN applications such as~\cite{ref22}: health-care, environment, agriculture, public safety, military, transportation systems, and industry applications.} In this paper we design a protocol that focuses on the area coverage problem with the objective of maximizing the network lifetime. Our proposition, the @@ -116,8 +116,7 @@ framework of the DiLCO approach and the coverage problem formulation. In this paper we made more realistic simulations by taking into account the characteristics of a Medusa II sensor ~\cite{raghunathan2002energy} to measure the energy consumption and the computation time. We have implemented two other -existing approaches (a distributed one, DESK ~\cite{ChinhVu}, and a centralized -one called GAF ~\cite{xu2001geography}) in order to compare their performances +existing \textcolor{blue}{and distributed approaches}(DESK ~\cite{ChinhVu}, and GAF ~\cite{xu2001geography}) in order to compare their performances with our approach. We also focus on performance analysis based on the number of subregions. % MODIF - END @@ -244,7 +243,8 @@ less accurate according to the number of primary points. \label{main_idea} \noindent We start by applying a divide-and-conquer algorithm to partition the area of interest into smaller areas called subregions and then our protocol is -executed simultaneously in each subregion. +executed simultaneously in each subregion. \textcolor{blue}{Sensor nodes are assumed to +be deployed almost uniformly over the region and the subdivision of the area of interest is regular.} \begin{figure}[ht!] \centering @@ -257,7 +257,9 @@ As shown in Figure~\ref{fig2}, the proposed DiLCO protocol is a periodic protocol where each period is decomposed into 4~phases: Information Exchange, Leader Election, Decision, and Sensing. For each period there will be exactly one cover set in charge of the sensing task. A periodic scheduling is -interesting because it enhances the robustness of the network against node failures. \textcolor{blue}{Many WSN applications have communication requirements that are periodic and known previously such as collecting temperature statistics at regular intervals. This periodic nature can be used to provide a regular schedule to sensor nodes and thus avoid a sensor failure. If the period time increases, the reliability and energy consumption are decreased and vice versa}. First, a node that has not enough energy to complete a period, or +interesting because it enhances the robustness of the network against node failures. +% \textcolor{blue}{Many WSN applications have communication requirements that are periodic and known previously such as collecting temperature statistics at regular intervals. This periodic nature can be used to provide a regular schedule to sensor nodes and thus avoid a sensor failure. If the period time increases, the reliability and energy consumption are decreased and vice versa}. +First, a node that has not enough energy to complete a period, or which fails before the decision is taken, will be excluded from the scheduling process. Second, if a node fails later, whereas it was supposed to sense the region of interest, it will only affect the quality of the coverage until the @@ -291,10 +293,10 @@ and each sensor node will have five possible status in the network: An outline of the protocol implementation is given by Algorithm~\ref{alg:DiLCO} which describes the execution of a period by a node (denoted by $s_j$ for a sensor node indexed by $j$). At the beginning a node checks whether it has -enough energy to stay active during the next sensing phase. If yes, it exchanges +enough energy \textcolor{blue}{(its energy should be greater than a fixed treshold $E_{th}$)} to stay active during the next sensing phase. If yes, it exchanges information with all the other nodes belonging to the same subregion: it collects from each node its position coordinates, remaining energy ($RE_j$), ID, -and the number of one-hop neighbors still alive. Once the first phase is +and the number of one-hop neighbors still alive. \textcolor{blue}{INFO packet contains two parts: header and data payload. The sensor ID is included in the header, where the header size is 8 bits. The data part includes position coordinates (64 bits), remaining energy (32 bits), and the number of one-hop live neighbors (8 bits). Therefore the size of the INFO packet is 112 bits.} Once the first phase is completed, the nodes of a subregion choose a leader to take the decision based on the following criteria with decreasing importance: larger number of neighbors, larger remaining energy, and then in case of equality, larger index.