devices . Baeg initiated a smart home environment project for light-weight service robots to provide reliable services through the wireless sensor networks . Luo and his colleagues described a prototypical configuration for networked robot systems . Suh suggested a new intelligent home control system based on wireless sensor/ actuator network, which divided and assigned various home network tasks to appropriate components . Yu implemented a ubiquitous robotic space with sensor network based on ZigBee protocol . Wireless sensor and actor networks is a new network module. It is a dis- tributed sensor and control system which is developed from the wireless sensor network by add many kinds of actuators. It can finish the distributed information collec- tion and perform scheduled tasks . How to build a communication network which is high reliable and easy to be built is very important technology in the intelligent space oriented to home service robot, it is the basis for the robot to provide the intelligent service for the human. In the intelligent space oriented to home service robot, since ZigBee technology has the characters of lower speed, lower power and less complexity, it is adopted to build wireless sensor and actor network to transfer the environmental sensor data, the intelligent space com- mands and their feedback, so that the intelligent space and service robots can be closely connected.
LeDiR can recover from a single node failure at a time. Simultaneous node failures will occurs when a part of the deployment area becomes subject to a major hazardous event, e.g., hit by a bomb. Considering such a problem with collocated node failure is more complex and challenging in nature. As a next level we will implement Enhanced LeDir algorithm on Wireless sensor-actor network to overcome the multiple node failures problem with less amount of delay and to the system performance
In this subsection, we provide the details of the MLS protocol. In random waypoint model, after the actor reaches its destination (x0, y0) in one step, it should decide its mobility strategy in the next step including: the location of the next waypoint (x1, y1) and its speed v. Then the actor creates the update packet which includes: initial timestamp t0, the coordinate of its current location (x0, y0), the coordinate of the next waypoint (x1, y1), and the moving time τ as follows, Then the actor disseminates the update packet into the network. In MLS, we adopt the quorum-based approach (XYLS)  to disseminate the update packet. The update packet is forwarded to all the nodes located in the grids of the same column. That is, as shown in Fig.1, the actor sends the update packet in both north and south direction to reach the north and south boundaries through geographic routing. The sensor nodes located in the column become location servers and form an update quorum. They save the update packet for a given interval τ, which is calculated by Eqn. (1) and stored in the update packet. After τ slots, it discards the update packet and returns to idle state. When a source needs to transmit an event report to the actor, the event report is firstly transmitted along the east west direction, i.e. the query quorum in the network area. The query quorum is expected to intersect at least one location server in the update quorum.
is important to detect node failures and restore network connectivity as early as possible. Since WSANs are usu- ally deployed far away from the control center and are operated autonomously and unattended, it is difficult and inefficient to control the restoring process in a central- ized manner. Connectivity restoration therefore should be a distributed, localized, and self-healing process. In addi- tion, a rapid connectivity restoration is desired in order to reduce the baneful influence of node failures. More- over, the overhead such as the total travel distance and the total number of messages should be minimized consider- ing the limited energy supply. The average travel distance should be considered as well because one node travel- ing too far will consume too much energy and may cause another network disconnection. A node failure disrupt- ing network connectivity is called a cut vertex, which is difficult to identify in large-scale WSANs centralized and timely. Though there have been many distributed cut- vertex detection algorithms, they are time-consuming and resource-intensive. As a result, it is very challenging to restore network connectivity in a distributed, localized, and efficient manner.
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Wireless sensor actor networks are composed of sensor and actor nodes wherein sensor nodes outnumber resource-rich actor nodes. Sensor nodes gather information and send them to a central node (sink) and/or to actors for proper actions. The short lifetime of energy-constrained sensor nodes can endanger the proper op- eration of the whole network when they run out of power and partition the network. Energy harvesting as well as minimizing sensor energy consumption had already been studied. We propose a different approach for recharging sensor nodes by mobile actor nodes that use only local information. Sensor nodes send their energy status along with their sensed information to actors in their coverage. Based on this energy informa- tion, actors coordinate implicitly to decide on the timings and the ordering of recharges of low energy sensor nodes. Coordination between actors is achieved by swarm intelligence and the replenishment continues dur- ing local learning of actor nodes. The number of actors required to keep up such networks is identified through simulation using VisualSense. It is shown that defining the appropriate number of actor nodes is critical to the success of recharging strategies in prolonging the network lifetime.
In , the authors propose a model for evaluating the reliability of disjoint areas in a WSN subject to two types of failure events, namely, SN failures due to battery depletion and link failures. Their proposed approach depends on dividing the targeted RoI into disjoint areas or target regions. For each region, a reliability model is constructed using a reliability block diagram (RBD) , which depends on the number of SNs monitoring the target region, their relative location from the sink node, and the routing protocol used in the network. There are two drawbacks of the proposed reliability modeling pro- posed in . The first drawback is that the model does not provide a method by which the reliability of the en- tire WSN deployment can be evaluated in terms of the reliability of its regions. The second drawback is that the reliability modeling is carried out under the assumption that the probabilities of link failures are known and are constant throughout the lifetime of the WSN. This as- sumption is unrealistic since link quality is affected by numerous factors such as multi-path effects, shadowing (due to static and mobile obstacles), and interference. The effect of these factors on link quality varies signifi- cantly and rapidly in time and space  and hence, un- like SN-related factors, cannot be reduced to a constant probability of failure throughout WSN mission time. In , the authors consider the problem of evaluating the transmission reliability of cluster-based and mesh-based WSN deployments. They define transmission reliability as the ratio of the packets received by a destination node to the whole packets generated by the transmission for a given period of time. They present transmission reliabil- ity evaluation models for the uplink and downlink traffic based on the assumptions that SNs are not subject to any hardware failures and that SNs only fail when their initial battery energy is exhausted. Although the time- dependent models presented in  can help assess the transmission reliability over time for a given routing strategy, they are limited by the assumption that SNs cannot fail due to random hardware failures unrelated to battery exhaustion. Also, it is not possible to use the study in  to calculate or estimate the reliability of the WSN over a given mission time since coverage functionality is not considered.
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This paper elaborates the characteristics and functioning of each module of node architecture as well as of WSN architecture. The flexibility, fault tolerance, high sensing fidelity, low cost, and rapid deployment characteristics of sensor networks create many new and exciting application areas for remote sensing. In future, these wide ranges of application areas will make sensor networks an integral part of our daily life. In essence, sensor networks will provide the end user with intelligence and a better understanding of the environment. Although a great work has been done in relation with wireless sensor networks, till date and but still many efforts and research work is needed in the direction of design and security of WSN. REFERENCES
Low Energy Adaptive Clustering Hierarchy (LEACH) is thefirst hierarchical cluster-based routing protocol for wireless sensor network.In LEACH the nodes are partitions intoclusters and in each cluster there is a dedicated node withextra privileges called Cluster Head (CH). This CH createsand manipulates a TDMA (Time division multiple access) schedule for the other nodes (cluster member) of thatcluster. Those CHs aggregate and compress the sensing dataand send to base Station (BS) . Thus it extends thelifetime of major nodes as shown in Fig. 1.
Topology of the network is usually dynamic. Although in most cases nodes are not movable, lifetime of each node is different. And because of energy saving, RF range of each node is usually limited to nearest neighbors. This implies that communication with sink must be done by using point to point protocols. When a neighbor dies, node usually must find another route to send its data. There are many protocols addressing this issue , .
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ZigBee devices have a 64-bit and 16-bit address . Each ZigBee node has a 64- bit unique address which is allocated during manufacturing and it is a permanent address which uniquely identifies a node. Each ZigBee node is assigned a 16-bit address called the network address when the node joins a network. This address is unique to all the physical devices in a particular network. However, this type of address is not a permanent address like the 64-bit address. Apart from the coordinator, which has a 16-bit address of 0x0000 all other devices is assigned a randomly generated address from the coordinator or router node that allows the join [11, 15]. The 16-bit address can be changed, when it is discovered that two devices in the same network have the same 16-bit address and when a node leaves the network and rejoins the same network [9, 15]. ZigBee transmissions are sent using both the source and destination 16-bit network addresses . Since the 16-bit address is not permanent, it is a non-reliable method to identify a device but the routing tables on each node use the 16-bit address to determine how to route data across the network. A 64-bit address must be included in the transmissions of data to guarantee that data is delivered to the appropriate device [11, 9].
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G. Kalnoor and J. Agarkhed,  "QoS based multipath routing for intrusion detection of sinkhole attack in Wireless Sensor Networks.” As the number of nodes and size of the network increases, there will be rapid increase in internet traffic. In WSN, security is the major issue and needs to a system that can provide security. Intrusion Detection System (IDS) is the system which plays a vital role in security of a system. One of the major challenges of WSN is to provide consistent Quality of Service (QoS) such as reliability, congestion control, energy efficiency and end-to-end delay, by applying secured routing protocols along with detection of an intruder so that QoS of WSN does not get affected. In this research work, different routing protocols that are QoS based are discussed, to improve the overall performance of the network.
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WSN nodes are increasingly being deployed for a number of applications. The characteristic features of such nodes are their low cost, low energy consumption and deployment in geographically wide areas. The deployment scenarios vary from habitat monitoring, environment monitoring to plant equipment monitoring. The primary job of wireless communication standards is to do away with the hassle of wires and provide an almost maintenance-free network that delivers data. Since the nodes are wireless, powering them up from the main supply would be counter-productive. Supplying energy through batteries, energy scavenging (reuse) or other local energy sources (such as solar cells) are then the only viable options. However Batteries have limited capacity. Continuous operation will drain batteries and make the nodes inoperable. Frequent changes of batteries are not a practical option since a network might have over a hundred nodes spread over a large geographical area. In this context it becomes imperative that energy be saved so that regular 4AA batteries will last for a year or more. However to solve this quite difficult problem there is a need to take a holistic approach to the issue of energy management in sensor nodes and networks. The selection of components of the sensor node, the application program that runs in the node, the protocol configuration, routing algorithms all play a significant role in the energy consumption patterns and the energy conservation capability of the network. This work aims to identifying and quantifying energy saving methods in WSN. An important prerequisite to carry out this activity is to develop a methodology for the estimation of energy consumption in the individual WSN nodes and in the network as a whole. The purpose of this work will be to suggest node architecture, network infrastructure and protocol configuration that will all minimize the energy consumption of a WSN. In the optimization for minimal energy consumption care should
RSSI and LQI values easily, but when comparing the RSSI value to real RSS value (dBm); it became complicated due to mismatching. RSSI value is defined in datasheets but never explained as to how to calculate real RSS value in dBm. Although that, for Jennic WSN processor equipped in wireless sensor nodes , the signal strength (above the receiver sensitivity level) is displayed on the Master board using LEDs D1, D2, D3 and D4, as follows:
Abstract: For sensing and performing task wireless sensor network (WAS) is one of the technology. Security is an important subject nowadays in almost every network. Traditional WSNs are precious by various types of attacks like Attacks on security and authentication, Silent attacks on service integrity. Attacks on network availability. A wireless sensor netwok has important applications such as isolated environmental monitoring and target tracking. Some security issues and attacks need to be work upon. In this paper we discuss some of the issues and the denial of service attacks of security, security problems of WAS constructed on resource organized scheme and placement characteristics for designing a safe WAS.
which displays the present status of the temperature and humidity. But most of time such an alerting message could easily go unnoticed. So, it is better to log the data in a remote computer so that he can keep a track of the data. Another work in makes use of the alarming system for attending a patient, This temperature and humidity measurement sensor can fail if the user in charge is away for the situation where the emergency is taking place. A temperature and humidity sensors rise an alarm following it would be unnoticed. So, a robust device combining an alerting and data logging system is needed to avoid this kind of situation. The paper deals with sending the values of temperature to the environment where sensor is exposed to, by SMS for the user or the person in charge. Also, by creating microcontroller database, the design described in this paper can be used as a modification for alerting the user by giving an “ALERT SMS” when the temperature have a deviation from a critical value preset by the user. The system of server guard maintenance mechanism presented in the current paper is totally different as it doesn’t take into consider any software which has to be run in any of personal computer. The final product doesn’t take into consideration any high power consumption devices like laptop or personal computer. The response of our design when the temperature or humidity is out of range as defined by the user or the critical value preset by the user is better and more advanced as we have the provision for data logging as well as to alert the user in case of an alarming event.
To localize jammers in wireless sensor network is very challenging .As earlier it has been noted that in wireless sensor network the localization of jammers plays an important role as it causes an unintentional radio interference, or enable a wide range of defense strategies for combating malicious jamming attackers .In earlier , prior work there was use of indirect measurement like neighbouring ranges and hearing ranges which results in accuracy but unable to localize jammer on proper position.
Up to now, the protocols mainly focus on energy saving sacrificing; if necessary, some network performance for getting higher power efficiency. Most of the energy consumption inside sensor unit is used for computing and communication while the energy consumption of the sensing module is much lower. For keeping necessary communication , the sensor nodes should be effectively controlled. Some improvements of the routing protocol should be made in how to reduce the flood of redundant data in the network, how to improve the robustness of the network link and how to reduce sensor network energy consumption. Fig. 1 shows the energy consumption of nodes, e.g. Node Sleeping can greatly reduce the energy consumption, while the sending/receiving and idle time will consume a lot of energy.
According to the general basic structure of wireless sensor network node, it is known that, the hardware system of wireless sensor network node must include pro- cessor module, sensor module, wireless communication module and power module. Among them, the power module serves as the power supply function component to provide energy for the entire sensor node, and the remaining three functional modules of the sensor nodes are energy consuming modules. In addition, the debugger and the download program interface should be considered to be added in the hardware design. Considering the low power consumption, small size, low cost and high flexibility design requirements of the node, the overall framework design of the hardware sys- tem of sensor nodes is shown in figure 3.
There was a project named Code Blue Project by Astang Coupe in 1967. The wearable computer is made which was attached to the patient’s wrist. In this off-the-shelf wireless sensors are used to design a prototype such as Tmote sky platform. It form an ad hoc network with portable tablet PC. Sensors used in this project are wireless two-lead ECG, a wireless pulse oximeter sensor & a wireless electromyogram.
An insider cannot be prevented from participating in the network, but she should only be able to do so using the identities of the nodes she has compromised. Using a globally shared key allows an insider to masquerade as any (possibly even nonexistent) node. Identities must be verified. In the traditional setting, this might be done using public key cryptography, but generating and verifying digital signatures is beyond the capabilities of sensor nodes. One solution is to have every node share a unique symmetric key with a trusted base station. A pair of neighboring nodes can use the resulting key to implement an authenticated, encrypted link between them. In order to prevent an insider from wandering around a stationary network and establishing shared keys with every node in the network, the base station can reasonably limit the number of neighbors a node is allowed to have and send an error message when a node exceeds it. Thus, when a node is compromised, it is restricted to (meaningfully) communicating only with its verified neighbors. This is not to say that nodes are forbidden from sending messages to base stations or aggregation points multiple hops away, but they are restricted from using any node except their verified neighbors to do so. In addition, an adversary can still use a wormhole to create an artificial link between two nodes to convince them they are neighbors, but the adversary will not be able to eavesdrop on or modify any future communications between them.