In the opening set of models, we assess the effects of different movable vitality and traffic pressure on the APU performance and make a comparison, as well with sporadic signalling and two currently suggested modernizing schemes: Speed-based —SB — and Distance-based Signalling. The outcome of models indicates that the APU can smoothly comply with manoeuvrability and traffic pressure. In each active case, APU makes fewer or similar number of signals in the air as other signalling methods, but can attain superior performing ability in the usual end-to-end holdup, ratio of packet delivery and use of power. In the other set of simulations, we assess the APU performance in the concern of many real- world issues like a real broadcasting propagation replica and faults of localization. The broad model conclusion substantiates the dominance of our suggested scheme over other tactics. The significant rationale behind these enhancements in APU is that the signals produced in APU are more widely spread all across the network. Consequently, in APU, the nodes placed in important spots, which are accountable for forwarding most of the data in the network have a modern, well-updated vision of their local topology, and as a result they offer rather enhanced performance.
Geographicrouting (also called geo routing or position-basedrouting) is a routing principle that relies on geographic position information. It is mainly proposed for wireless networks and based on the idea that the source sends a message to the geographic location of the destination instead of using the network address. The idea of using position information for routing was first proposed in the 1980s in the area of packet radio networks. And interconnection networks. Geographicrouting requires that each node can determine its own location and that the source is aware of the location of the destination. With this information a message can be routed to the destination without knowledge of the network topology or a prior route discovery. In geographicroutingmobility support can be facilitated. Since each node sends its coordinates periodically, all its neighbors update their routing tables accordingly thus all nodes aware of its alive neighbor nodes. It is scalable. The size of routing table depends on network density not on network population. Hence wider networks consisting of thousands of nodes can be realized without cluster formation. Minimum overheads are introduced. The only information needed is the location of neighbors. Only localized interactions take place. Hence bandwidth is economized. The processing and transmission energy is saved and the dimensions of routing table are decreased.
the velocity of nodes and triggers adaptive beacons based on predicted position and actual position coordi- nates of a node. This rule (discussed in next sections) helps the nodes to maintain neighbor lists with least vel- ocity. A least velocity, i.e., slower node, can be consid- ered more reliable than the faster node as it stays in the vicinity of a node for more time. After identifying the routes, each node selects two nodes as next hop and di- vides the load among them. Our proposed work (LAPU) thus concentrates on load balancing technique for geo- graphical routing in MANETs considering the conges- tion state and mobility of a node and provides a solution to overcome the problems in load balancing phenomena. The rest of the paper is organized as follows. Section 2 briefly explains APU algorithm. Section 3 throws light upon the Ad hoc On-demand Multipath Distance Vector routing (AOMDV). This protocol has become popular be- cause it is based on providing a multipath solution to avoid congestion or link failures in AODV routing protocol. Thus, AOMDV has been considered to compare the per- formances of APU and LAPU. Sections 4 and 5 provide the information about LAPU. The performance analyses of the three algorithms are given in the Section 6. Finally, the conclusion and the future work are discussed in Section 7.
Wireless sensor network (WSN) is a system composed of a large number of low-cost micro-sensors. This network is used to collect and send various kinds of messages to a base station (BS). WSN consists of low-cost nodes with limited battery power, and the battery replacement is not easy for WSN with thousands of physically embedded nodes, which means energy efficient routing protocol should be employed to offer a long-life work time. To achieve the aim, we need not only to minimize total energy consumption but also to balance WSN load. Researchers have proposed many protocols such as LEACH, HEED, PEGASIS, TBC and PEDAP. In this paper, we propose a General Self-Organized Tree-Based Energy-Balance routing protocol (GSTEB) which builds a routing tree using a process where, for each round, BS assigns a root node and broadcasts this selection to all sensor nodes. Subsequently, each node selects its parent by considering only itself and its neighbors' information, thus making GSTEB a dynamic protocol. Simulation results show that GSTEB has a better performance than other protocols in balancing energy consumption, thus prolonging the lifetime of WSN.
Figure 5 shows the packet delivery ratio (PDR) against the number of nodes in the network for MGEGR, GEGR, LAR, and DYMO protocols. Due to the nature of GEGR protocol as a unipath routing scheme, forwarded pack- ets encounter higher number of holes, as shown in Figure 6, since they do not have other alternative paths to se- lect from. This situation explains the low PDR for low node densities compared to MGEGR. However, MGEGR protocol is more stable and able to maintain high PDR performance against increasing number of nodes, due to its reliance on disjoint multi-paths from the source to the sink and its ability to avoid discovered paths with holes. Although LAR achieves better PDR performance than MGEGR, GEGR and DYMO for number of nodes equal to 100, it cannot sustain high packet delivery ratios when the number of nodes becomes greater than 150 in the network, due to collisions and loss of packets. In regard to DYMO, Figure 5 shows that its performance is completely inappropriate to dense wireless sensor networks with periodic transmission of data to the sink. The continuous drop in DYMO PDR with the increasing number of nodes indicates the excessive effect of collisions on the transmitted packets, where the source nodes are forced to continuously apply the route dis- covery process.
In geographical routing, the radical departure from topology basedrouting is the employment of physical location information, and the elimination of the topology storage dependency. In geographical routing, each device forward its physical location information to the radio neighbors and ensures the neighbors’ connectivity periodically using beacon messages. Instead of attempting to build and maintain the communication routes to the destination, an appropriate router is selected from the neighbor list. Hence, there is no need to consider the global topology information. In addition, the geographical routing eliminates the expensive control messages due to the removal of end-to-end route construction. The geographicrouting requires accurate location information, but it is difficult when the network has a large number of mobile devices resulting in highly dynamic network topology. There is a need to evaluate the different mobility model and their impact on the geographicrouting performance to handle the dynamic behavior of network topology. To describe the effect of node mobility and mobility model for geographical routing, this work takes GPSR  and POR .
Abstract—A Routing network is proposed for wireless sensor networks to deliver the information packets in a reliable and timely manner. Most existing routing protocol abuses the multipath routing to ensure both reliability and delay QoS limitations in WSNs. However, the multipath routing methodology experiences a energy cost and computation delay at each hop. The primary goal of the project is to augment the network lifetime and improve the Qos routing for both reliability and delay in WSNs. To accomplish the Qos routing by this project proposes an opportunistic information transfer at per hop communication. This plan is called Efficient QoS-aware GOR (EQGOR) protocol. It utilizes only hop prerequisites to get and keep up at a low overhead cost. In the event that the hop prerequisite can be accomplished at every hop, the end-to-end QoS necessity can likewise be met with a higher probability. EQGOR selects and prioritizes the forwarding candidate indicated by specific QoS prerequisites that are communicated as far as reliability and delay of single-hop packet advance and attains to the delicate QoS provisioning. In the event that a packet is effectively received by a percentage of the chose nodes, only the most priority need node among them is picked as the next hop forwarder. Thusly, this forwarding candidate turns into the real next- hop sender in an opportunistic manner. The forwarding methodology rehashes until the packet achieves the sink node. Changes of the end-to-end delay at increasing the reliability constraint from 0.6 to 0.99, EQGOR is less influenced by the changes of the reliability requirement and node density. Together with its shorter end-to-end delay, lower communication cost and higher delivery ratio, we conclude that compared with the multipath routing approach, all the performances are greatly improved by exploiting the GOR for QoS provisioning in WSNs. This project contributes the component is applicable for mobile sensor network. Execution of the proposed procedure is led utilizing Network Simulator to evaluate delay, throughput and energy effectiveness.
The combination of vehicles and wireless communica- tion has created a promising technology of vehicular ad hoc networks (VANETs) . A wide range of appealing applications can be developed with VANETs, from driv- ing safety  to urban monitoring . Compared to static sensor networks [4-6], a network of mobile vehicles can cover larger area and provide useful sensing informa- tion about the surrounding environment. VANETs exhibit some unique characteristics. First, vehicles have high mobility and the topology of a VANET can be changing quickly over time. The connection between vehicles is fre- quently disrupted. Second, being spread over a vast area, the vehicles may be unable to form a connected network. Third, the vehicles are distributed with different densities. These characteristics together suggest that most exist- ing designs and solutions for traditional network systems,
Number of information sent by the nodes in the network can have a big impact on the probability of information de- livery. Unfortunately, a large number of information sent is associated with greater overhead ratio, these two metrics are dependent on each other. It would be good to know how information that are sent by the nodes affect the number of delivered information. Therefore, we introduced a new metric defined as the efficiency of the algorithm. Efficiency is the ratio of the number of the information delivered to the number of all transmissions of the information. The results of the efficiency of the tested algorithms are shown in Fig. 3. As the graph shows, the proposed algorithm achieves the highest degree of efficiency. A high value has also Spray and Wait. The lowest value has Epidemic routing because of the very large number of transmissions. Prophet algorithm, which reached a very high probability of information delivery, also due to the large number of transmissions has a very low efficiency
Mobile Ad hoc Networks (MANETs) and Wireless Sensor Networks (WSNs) are two special classes of wireless networks. WSNs are termed as the predecessor of MANETs. A WSN is composed of tiny and low-cost sensor nodes (SNs) having limited resources in-terms of processing, memory and energy. These networks are self configurable and the communication is carried using wireless links. Applications of these networks include temperature and humidity monitoring, pollution monitoring in environmental applications, supply chain management applications, body area networks, pressure and speed monitoring in automotive, pungent gas or chemical detection in industries, target detection in military etc. . In general, these networks are deployed in open, unattended and hostile environments to monitor and report the events. Due to the openness of SNs in WSNs, they are subject to eavesdropping, physical tampering, etc., which leads to various security attacks. Research studies in MANETs have shown that packet delivery percentage can substantially decrease when malicious activities are present in the network. Cryptography methods can be utilized to mitigate security attacks posed by malicious nodes in the network and to provide authentication and secrecy. However, these methods require thousands of multiplication and addition operations to implement a single security operation. In addition, they are not efficient in identifying security attacks posed by an adversary from outside of the network. In other words, cryptography can aid communication security rather than routing security. Due to this reason, cryptographic methods are not suited for routing in resource constrained sensor networks. A human behavior pattern called trust has been widely used by the researchers to aid routing security. Trust is the measure of belief by a node about the behavior of its neighboring nodes. This trust is formed by the assessment of cooperation and coordination received from the neighboring nodes in executing network activities such as packet forwarding, acknowledgements etc. . The trust value of a neighboring node will be incremented by one unit if node exhibits the positive behavior. Otherwise the related trust value is decremented by one unit. Along with these trust values, special values called weights are assigned to network activities. Lastly, a trust model computes absolute trust values of the neighboring node as the sum of products of weight and trust value of corresponding network activities.
A mobile ad hoc network (MANETs) is a self-configuring, wireless, low range/power, mobile infrastructure less network where each participating node itself manages the communication over the network. For handling the communication among all nodes (mobile) routing protocols should be capable enough to handle mobility, scalability, and dynamicity of the nodes in mobile ad hoc network. Routing protocols may classify as Flat, Hierarchical and Geographical routing protocols. Flat routing may deploy as Proactive/Table driven protocol (routing information maintained in a table prior to transmission occurrence, eg. FSR, TORA, TBRPF etc.) and Reactive/ On-Demand protocols (manage the dynamic routing information with reduction in communication overhead eg. AODV, DSR etc.).  Hierarchical routing protocols organized the participating nodes in scalable group based efficient arrangements as in CGSR, ZRP, and LANMAR etc. Both of these routing approaches do not account for the location information of the mobile nodes as it accounted by Geographical routing mechanism in which GPS is responsible for conveying location information of each node present over the network. [2,7] Using this physical location of the nodes routing overhead can be reduced and efficiency of routing protocols can be improved as in GPSR, LAR etc. In these protocols location information (obtain via GPS) is used for route discovery mechanism which ultimately reduces the search space and limit the flooding area and allows the routing of information over a refined path. 
Among all those DTN routing strategies mentioned above, ER is chosen for group mobility for two reasons. First, we consider the group mobility scenario where neither the location of the mobile node nor the future movement plan could be known or available. Impor- tantly, ER does not require any prior node location or node moving information. Second, although ER is a resource-consuming protocol, especially in the network with large node number, it can achieve optimal perfor- mance given the adequate resources, such as the buffer size and bandwidth. In fact, the sensitivity of ER to the resource in terms of buffer could be largely alleviated in group mobility by treating each group as a single node and disseminating one copy of one packet to one group, because the group number is generally much less than the node number in the network. Due to these reasons, our proposed G-ER is designed based on ER.
The advantage of position-basedgeographicrouting over other ad hoc routing protocols is the fact that nodes require only knowledge about the local neighborhood and the destination’s location instead of global route topology. Therefore, position-basedrouting is better suited for networks with a certain degree of mobility. Thus this paper have proposed a cross-layer protocol CoopGeo based on geographic information to effectively integrate the Network, MAC, and PHY layers for cooperative wireless ad hoc networks. Simulation demonstrates that the selected best relay contributes to the minimum SER at the destination as compared to other relays. It reveals that the smaller the selection metrics, the better the resulting SER performances. Also presented and analyzed a new contention access method to reduce collisions in scalable sensor network. Proposed method uses a fixed contention window, and changes the contending probability. Error performance is also evaluated based on contention and probability error rate.
information and interface to real world. Networking and transport layers play a key role in the system because they are responsible to handle reliable delivery of messages across the network. These layers implement a number of functionalities, which are shown in Figure 1(b). User Datagram Protocol (UDP) and Transmission Control Pro- tocol (TCP) can be used for transport layer protocols. The basic transport protocol  is a UDP-like trans- port protocol commonly used with geographicrouting. In addition, ITS specific transport protocols have been designed to cope with the characteristics of vehicular traf- fic, for example, vehicular transport protocol (VTP)  and Vehicular Information Transfer Protocol (VITP) . Network layer implements functionalities such as rout- ing, addressing, mobility management and other. Position- basedrouting is usually used for ad hoc communications among vehicles and roadside units (RSUs) and relies on geo-addressing scheme as specified in . In addition, geo-addressing requires a ‘translation’ mechanism from IP addresses to geo-networking addresses. This is usually done by a GeoNetworking IPv6 Adaptation SubLayer . In position-basedrouting protocols, each node maintains a local database which stores the position of its neighbours that is learnt through the neighbour discovery mechanism. Moreover, position-basedrouting relies on a location ser- vice to identify the position of a destination node. Each node can determine its own location and get navigation information from the facility layer in Figure 1(a). Finally, an important part of system’s architecture, which spans alongside all layers, is security. The security requirements include aspects such as data integrity, authentication and privacy, as well as detection and resilience against attacks. The security mechanisms for the proposed system archi- tecture are described in .
The AODV is an on-demand or reactive MANET routing protocol . The number of routing messages in the network is reduced due to its reactive approach which makes it use the bandwidth more efficiently. But in highly mobile and heavily loaded networks, protocol overhead may increase. Furthermore, due to reactive approach it is more immune to the topological changes witnessed in the MANET environment. As a result, the AODV offers quick adaptation to dynamic link conditions, low CPU processing and memory overhead, low network utilization, and determines unicast routes to destinations within the MANET. It also allows mobile nodes to obtain routes quickly for new destinations, and does not require nodes to maintain routes to destinations that are not in active communication. One distinguishing feature of AODV is its use of a destination sequence-number (DSN) for each route entry. The DSN is created by the destination to be included along with any route information it sends to requesting nodes. Use of DSN in routing protocols ensures loop freedom. Hence, the operation of AODV is loop- free, and by avoiding the Bellman-Ford "counting to infinity" problem offers quick convergence when the MANET witnesses topological changes.
Simulations are for networks of 50, 112, and 200 nodes with 802.11 WaveLAN radios, with a nominal 250-meter range. The nodes are initially placed uniformly at random in a rectangular region. All nodes move according to the random waypoint model, with a maximum velocity of 20 m/s. Pause times simulated of 0, 30, 60, and 120 seconds, the highest mobility cases, as they are the most demanding of a routing algorithm.
Animal Habitat monitoring can be better carried out remotely without active human interruption. The designed sys- tem should provide data that facilitates proactive measures to prevent the spreading of health hazards among animals, detect intruding poachers and track the locomotive behavior of animals in their habitat. In this work, reliable and resilient cost effective routing solutions are implemented addressing Passive and Active Mobility requirements through embedded plat- forms such as Raspberry PI and Arduino. For addressing passive mobility requirement, an alternate to Zigbee mesh routing protocol (Modified AODV) namely a Greedy BasedGeographic Forwarding with Delay Tolerant approach is implemented. Nodes tagged on to animals are equipped with GPS and IEEE 802.15.4 wireless transceiver to transmit the location information along with sensed parameters either via single hopor maximum forwarding progress neighbor (animal) towards sink in multi-hop manner. The payload is buffered until a forwarding neighbor (neighboring animal) or sink is detected in transmission range to handle void issues in deployed monitoring area. Sink is assumed to be stationary and this solution is designed to address application requirement that demands to track the locomotive behavior of animals via passive mobility. It isachievedthrough802.15.4MACaddressbasedtagging.However, for Active Mobility requirement, a routing solution is implemented where nodes are equipped with camera and driven through stepper motor(s). These spatially distributed nodes are made to move across the field to capture the detected animal images and transmit through Wi-Fi network to the gateway directly or through Optimized Link State Routing Approach (OLSR). These active mobile nodes run High Speed Multimedia Stack in Raspberry PI and transmit the compressed images by applying Discrete Cosine Transform (DCT) to reduce the band width and communication cost in addition to resolving congestion in a dense deployment.
A very early mention of power exhaustion can be found in, as “sleep deprivation torture”  . As per the name, the proposed attack prevents nodes from entering a low-power sleep cycle, and thus depletes their batteries faster. Newer but only offers rate limiting and elimination of insider adversaries as potential solutions. Malicious cycles (routing loops) have been briefly mentioned  , but no effective defenses are discussed other than increasing efficiency of the underlying. MAC and routing protocols or switching away from source routing. Since Vampire attacks rely on amplification, such solutions may not be sufficiently effective to justify the excess load on legitimate nodes. Other work on denial of service in ad- hoc wireless networks has primarily dealt with adversaries who prevent route setup, disrupt communication, or preferentially establish routes through themselves to drop, manipulate, or monitor packets [9, 10 ] . The effect of denial or degradation of service on battery life and other finite node resources has not generally been a security consideration, making our work tangential to the research mentioned above. Protocols that define security in terms of path discovery success, ensuring that only valid network paths are found, cannot protect against Vampire attacks, since Vampires do not use or return illegal routes or prevent communication in the short term. Deng et al. discuss path-based DoS attacks and defenses in  . While this strategy may protect against traditional DoS, where the
Low-latency geographicrouting for asynchronous energy-harvesting WSNs  uses both geographic and duty-cycle information about the neighbour of the node, to route data efficiently and quickly up to the sink. Energy harvesting is a new technology of getting energy from environmental sources like, solar energy, temperature variations, kinetic energy and vibration. As the energy harvested from the environment can be different from node to node because of the node position and surroundings so each node follows a duty cycle in the protocol. In the geographical routing protocols nodes routes the data packets based on in- formation about its neighbours and the sink. Knowledge range (KR) is the topo- logical extent of this information. A larger KR can give more nearly optimal path, but acquiring and maintaining this topological information require more energy. Protocol uses D-APOLLO algorithm which periodically updates both the KR and duty cycle (DC) of the nodes while considering the information about the local energy budget for the next period. Using this information (KR and DC) in the protocol reduces the end to end delay to the sink.
Abstract-Geographicrouting is a technique to deliver a message to a node in an adhoc network over multiple hops by means of position information. Each node updates its own position by the use of GPS or any localization techniques and forwards the data to the node closest to the destination. All nodes send a beacon known as location update message to its one hop neighbors. The most commonly used geographicrouting algorithm is Greedy Perimeter Stateless Routing Protocol (GPSR) where communication is unicast. In our proposed work, the frequency of updating the location is dynamic which is based on mobility dynamics and over hearing the transmission by its neighbors. We also use dual path communication with dynamic position update where the selection of neighbors is based on the most optimal neighbor and the node which have lowmobility thus increasing the QoS.