From: couturie Date: Wed, 14 Aug 2013 09:04:29 +0000 (+0200) Subject: first English corrections X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/UIC2013.git/commitdiff_plain/6faf6604efe05771971cadb7bb476b0bb8bc0b86 first English corrections --- diff --git a/bare_conf.tex b/bare_conf.tex index 83d454a..3a76a98 100755 --- a/bare_conf.tex +++ b/bare_conf.tex @@ -55,12 +55,12 @@ Email: ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel, michel.salomon \begin{abstract} One of the fundamental challenges in Wireless Sensor Networks (WSNs) -is coverage preservation and extension of the network lifetime +is the coverage preservation and the extension of the network lifetime continuously and effectively when monitoring a certain area (or region) of interest. In this paper a coverage optimization protocol to improve the lifetime in heterogeneous energy wireless sensor networks is proposed. The area of interest is first divided into subregions -using a divide-and-conquer method and then scheduling of sensor node +using a divide-and-conquer method and then the scheduling of sensor node activity is planned for each subregion. The proposed scheduling considers rounds during which a small number of nodes, remaining active for sensing, is selected to ensure coverage. Each round @@ -79,12 +79,12 @@ network lifetime and improve the coverage performance. \noindent Recent years have witnessed significant advances in wireless communications and embedded micro-sensing MEMS technologies which have -made emerge wireless sensor networks as one of the most promising +led to the emergence of wireless sensor networks as one of the most promising technologies~\cite{asc02}. In fact, they present huge potential in several domains ranging from health care applications to military applications. A sensor network is composed of a large number of tiny sensing devices deployed in a region of interest. Each device has -processing and wireless communication capabilities, which enable to +processing and wireless communication capabilities, which enable it to sense its environment, to compute, to store information and to deliver report messages to a base station. %These sensor nodes run on batteries with limited capacities. To achieve a long life of the network, it is important to conserve battery power. Therefore, lifetime optimisation is one of the most critical issues in wireless sensor networks. @@ -99,13 +99,13 @@ is desirable that a WSN should be deployed with high density because spatial redundancy can then be exploited to increase the lifetime of the network. In such a high density network, if all sensor nodes were to be activated at the same time, the lifetime would be reduced. To -extend the lifetime of the network, the main idea is to take benefit -from the overlapping sensing regions of some sensor nodes to save +extend the lifetime of the network, the main idea is to take advantage +of the overlapping sensing regions of some sensor nodes to save energy by turning off some of them during the sensing phase. Obviously, the deactivation of nodes is only relevant if the coverage -of the monitored area is not affected. Consequently, future software +of the monitored area is not affected. Consequently, future softwares may need to adapt appropriately to achieve acceptable quality of -service for applications. In this paper we concentrate on area +service for applications. In this paper we concentrate on the area coverage problem, with the objective of maximizing the network lifetime by using an adaptive scheduling. The area of interest is divided into subregions and an activity scheduling for sensor nodes is @@ -113,10 +113,10 @@ planned for each subregion. In fact, the nodes in a subregion can be seen as a cluster where each node sends sensing data to the cluster head or the sink node. Furthermore, the activities in a subregion/cluster can continue even - if another cluster stops due to too much node failures. + if another cluster stops due to too many node failures. Our scheduling scheme considers rounds, where a round starts with a discovery phase to exchange information between sensors of the -subregion, in order to choose in suitable manner a sensor node to +subregion, in order to choose in a suitable manner a sensor node to carry out a coverage strategy. This coverage strategy involves the solving of an integer program which provides the activation of the sensors for the sensing phase of the current round. @@ -139,8 +139,8 @@ suggestions for future works in Section~\ref{sec:conclusion}. in the literature for the coverage lifetime maximization problem, where the objective is to optimally schedule sensors' activities in order to extend network lifetime in a randomly deployed network. As -this problem is subject to a wide range of interpretations, we suggest -to recall main definitions and assumptions related to our work. +this problem is subject to a wide range of interpretations, we have chosen +to recall the main definitions and assumptions related to our work. %\begin{itemize} %\item Area Coverage: The main objective is to cover an area. The area coverage requires @@ -156,14 +156,14 @@ to recall main definitions and assumptions related to our work. The most discussed coverage problems in literature can be classified into two types \cite{ma10}: area coverage (also called full or blanket coverage) and target coverage. An area coverage problem is to find a -minimum number of sensors to work such that each physical point in the +minimum number of sensors to work, such that each physical point in the area is within the sensing range of at least one working sensor node. Target coverage problem is to cover only a finite number of discrete points called targets. This type of coverage has mainly military applications. Our work will concentrate on the area coverage by design and implementation of a strategy which efficiently selects the active nodes that must maintain both sensing coverage and network -connectivity and in the same time improve the lifetime of the wireless +connectivity and at the same time improve the lifetime of the wireless sensor network. But requiring that all physical points of the considered region are covered may be too strict, especially where the sensor network is not dense. Our approach represents an area covered @@ -175,10 +175,10 @@ simultaneously). {\bf Lifetime} Various definitions exist for the lifetime of a sensor -network~\cite{die09}. Main definitions proposed in the literature are -related to the remaining energy of the nodes or to the percentage of -coverage. The lifetime of the network is mainly defined as the amount -of time that the network can satisfy its coverage objective (the +network~\cite{die09}. The main definitions proposed in the literature are +related to the remaining energy of the nodes or to the coverage percentage. +The lifetime of the network is mainly defined as the amount +of time during which the network can satisfy its coverage objective (the amount of time that the network can cover a given percentage of its area or targets of interest). In this work, we assume that the network is alive until all nodes have been drained of their energy or the @@ -191,11 +191,11 @@ transmit information on an event in the area that it monitors. Activity scheduling is to schedule the activation and deactivation of sensor nodes. The basic objective is to decide which sensors are in -what states (active or sleeping mode) and for how long, such that the +what states (active or sleeping mode) and for how long, so that the application coverage requirement can be guaranteed and the network lifetime can be prolonged. Various approaches, including centralized, distributed, and localized algorithms, have been proposed for activity -scheduling. In the distributed algorithms, each node in the network +scheduling. In distributed algorithms, each node in the network autonomously makes decisions on whether to turn on or turn off itself only using local neighbor information. In centralized algorithms, a central controller (a node or base station) informs every sensors of @@ -206,40 +206,39 @@ the time intervals to be activated. Some distributed algorithms have been developed in~\cite{Gallais06,Tian02,Ye03,Zhang05,HeinzelmanCB02} to perform the scheduling. Distributed algorithms typically operate in rounds for -predetermined duration. At the beginning of each round, a sensor -exchange information with its neighbors and makes a decision to either +a predetermined duration. At the beginning of each round, a sensor +exchanges information with its neighbors and makes a decision to either remain turned on or to go to sleep for the round. This decision is -basically based on simple greedy criteria like the largest uncovered +basically made on simple greedy criteria like the largest uncovered area \cite{Berman05efficientenergy}, maximum uncovered targets \cite{1240799}. In \cite{Tian02}, the scheduling scheme is divided into rounds, where each round has a self-scheduling phase followed by -a sensing phase. Each sensor broadcasts a message containing node ID -and node location to its neighbors at the beginning of each round. A +a sensing phase. Each sensor broadcasts a message containing the node ID +and the node location to its neighbors at the beginning of each round. A sensor determines its status by a rule named off-duty eligible rule which tells him to turn off if its sensing area is covered by its neighbors. A back-off scheme is introduced to let each sensor delay the decision process with a random period of time, in order to avoid -that nodes make conflicting decisions simultaneously and that a part -of the area is no longer covered. +simultaneous conflicting decisions between nodes and lack of coverage on any area. \cite{Prasad:2007:DAL:1782174.1782218} defines a model for capturing the dependencies between different cover sets and proposes localized heuristic based on this dependency. The algorithm consists of two phases, an initial setup phase during which each sensor computes and -prioritize the covers and a sensing phase during which each sensor +prioritizes the covers and a sensing phase during which each sensor first decides its on/off status, and then remains on or off for the rest of the duration. Authors in \cite{chin2007} propose a novel distributed heuristic named Distributed Energy-efficient Scheduling for k-coverage (DESK) so that the energy consumption among all the sensors is balanced, and network lifetime is maximized while the -coverage requirements is being maintained. This algorithm works in +coverage requirement is being maintained. This algorithm works in round, requires only 1-sensing-hop-neighbor information, and a sensor decides its status (active/sleep) based on its perimeter coverage computed through the k-Non-Unit-disk coverage algorithm proposed in \cite{Huang:2003:CPW:941350.941367}. -Some others approaches do not consider synchronized and predetermined +Some other approaches do not consider a synchronized and predetermined period of time where the sensors are active or not. Indeed, each -sensor maintains its own timer and its time wake-up is randomized +sensor maintains its own timer and its wake-up time is randomized \cite{Ye03} or regulated \cite{cardei05} over time. %A ecrire \cite{Abrams:2004:SKA:984622.984684}p33 @@ -262,7 +261,7 @@ to divide/organize the sensors into a suitable number of set covers where each set completely covers an interest region and to activate these set covers successively. -First algorithms proposed in the literature consider that the cover +The first algorithms proposed in the literature consider that the cover sets are disjoint: a sensor node appears in exactly one of the generated cover sets. For instance, Slijepcevic and Potkonjak \cite{Slijepcevic01powerefficient} propose an algorithm which @@ -279,7 +278,7 @@ whole region of interest. Abrams et al.~\cite{Abrams:2004:SKA:984622.984684} design three approximation algorithms for a variation of the set k-cover problem, where the objective is to partition the sensors into covers such that the number -of covers that include an area, summed over all areas, is maximized. +of covers that includes an area, summed over all areas, is maximized. Their work builds upon previous work in~\cite{Slijepcevic01powerefficient} and the generated cover sets do not provide complete coverage of the monitoring zone.