collection m ethods, this category can be divided into two subcategories: beacon based and pressure based. Beacon- based subcategory employs beacon messages to assign special inform ation such as dynamic address to each node in order to identify the positive progress toward the sink, while in pressure-based subcategory only the depth information measured locally by pressure sensor can be used for identi fying the positive progress area. A deep description of these subcategories is provided in the following subsections. 3.2.1. Beacon-Based Routing. In the beacon-based subcate gory, the positive progress area toward the sink is identified based on special inform ation about the netw ork such as address which is obtained by sending periodical beacon messages from the surface ofwater to the bottom. The various inform ation is employed in different protocols to identify the positive progress toward the sink. For example, in [21, 42], dynamic address is used to identify the neighboring nodes with positive progress toward the sink, while the distance to sink is employed in . These protocols usually composed of two phases, namely, inform ation acquisition phase and data forwarding phase. In the first one, the surface buoys periodically send beacon message to the bottom of water. The beacon message is received by each neighbor node of surface buoy, and it updates its inform ation and beacon message. Then, the node broadcasts the updated message to its neighbor nodes and so on. Finally, all nodes earn the desired information. In the second phase, the inform ation obtained in the previous phase can be used for identifying the neighbor nodes with positive progress toward the sink and employing greedy routing m ethod in UWASNs. It should be noted that due to high mobility of nodes by water current in underwater environments, the inform ation acquisition phase should be done in short intervals, which causes a significant increase in the netw ork overhead. As a result, obtaining desired inform ation for greedy routing can be too expensive in high dynamic topology networks such as UWASNs. A num ber of protocols belonging to this category are described as follows.
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In our work, we propose a new weak greedy routing method. It does not need the geographic location. It establishes a tree-based graph embedding rather than a greedy embedding. In the tree-based graph embedding, every node is assigned an interval label: [i, r]. Based on nodes labels, we design a new greedy routing algorithm TGR (Tree-based Greedy Routing). When the current node cannot find a next hop node by the greedy rule, it uses a default rule to find a next hop node. It is guaran- teed that TGR algorithm is a loop-free routing protocol. It means that by using TGR algorithm, any source can find a path to any destination, while there’s no node appears along this path twice or more. Another interesting point is that the source node can route to the destination even it only knows part of the label of the destination. By extensive evaluation, our algorithm satisfies small path stretch factor and small load balance factor.
In recent years, vehicular communications are one of the hottest research topics. It has also gained much attention in industry as well as academia. Vehicular Ad Hoc Networks (VANETs) are advances of the wireless communication technologies. Routing is one of the key research issues in VANETs as long as it plays an important role in public safety and commercial applications. In VANET, routing of data is a challenging task due to high speed of nodes (i.e., vehicles) movement and rapidly changing topology. Recent research showed that existing routing algorithm solutions for Mobile Ad Hoc Networks (MANETs) are not able to meet the unique requirements of vehicular networks. In this paper, we propose Gateway Node-Based Greedy Routing (GNGR), a reliable greedy position-based routing approach algorithm. In GNGR, we forward the packets to any of the nodes in the corner of the transmission range of source/forwarding node as most suitable next hop. With this consideration, the nodes move towards the direction of the destination. We propose Dynamic Transition Mobility Model (DTMM) to evaluate our routing technique. This paper gives a complete description of our packet forwarding approach and simulation results. The simulation results are carried out based on Packet Delivery Ratio (PDR). Our routing technique is compared with other routing techniques; the PDR is improved significantly compared with other routing techniques of VANET.
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This algorithm improves upon GSR by eliminating the requirement of an external static street map for its operation. In GPCR , the typical destination-based greedy forwarding strategy is modiﬁed such that it only routes messages along streets. In this way, routing decisions are only made at street intersections. As such, the goal is to forward messages to nodes at an intersection, rather than forwarding them to a node that is already past the intersection. These nodes that are located within the area of an intersection are called “coordinators”. A coordinator periodically broadcasts its role along with its position information, and nodes can determine whether they are coordinators. Once a packet reaches a coordinator a decision has to be made about the street that the packet should follow. This is done in a greedy fashion: the neighbouring node with the largest progress towards the destination is chosen. As a consequence, the repair strategy of GPCR consists of two parts:
The deployment of node is dependent on the application and the type of the WSN and is in self- organizing networks, that, a reactive protocol is highly beneficial and so will be the proposed protocol DGRP. However, since the formation of obstacles can also occur in deterministic networks, DGRP can also be implemented in deterministic networks, but, as expected, the delay overhead will be more than other deterministic proactive routing protocols. Considering the constraints mentioned above, this article proposes a reactive protocol that aims at obstacle avoidance while still providing shortest path routing. Considering a Query driven wireless sensor network, firstly, the Path Determination Phase initiates which aims at determining the best path from the sink to the concerned node, using flooding algorithm. Fig.-1 depicts the optimum path selection. After that, the Path Selection Phase is initiated, where in the most optimum path is chosen from the set of determined paths using Greedy algorithm. The data is routed along this path and the sensor waits for an acknowledgement from the sink. If the acknowledgement is received within the expected time, the transmission ends, else, the next best path is chosen for routing. Figure 2 shows one such example of multiple possible paths. The designing of wireless sensor network elaborated by Singh et al. . The basic approach of DGRP is as mentioned below.
ABSTRACT- Vehicular Ad hoc Networks (VANET) are highly mobile wireless ad hoc networks that provides communication between vehicles. As a promising technology it plays an important role in public safety communications and commercial applications. Due to the rapidly changing of topology and high speed mobility of vehicle, routing of data in vanet becomes a challenging task. One of the critical issues of VANETs are frequent path disruptions caused by high speed mobility of vehicle that leads to broken links which results in low throughput and high overhead . This paper argues with how to maintain the reliable link stability between the vehicles without any packet loss using two separate algorithms besides position, direction, velocity and digital mapping of roads . In this paper we propose a reliable position based routing approach called Reliable Directional Greedy routing (RDGR) which is used to obtain the position, speed and direction of its neighboring nodes through GPS and as well as the well known Ad-Hoc on Demand Distance Vector(AODV) which includes vehicles position, direction, velocity with link stability. This approach incorporates potential score based strategy, which calculates link stability between neighbor nodes for reliable data transfer In this paper we use both RDGR and AODV approach in order to provide reliable link stability and efficient packet delivery ratio even in high speed mobility and changing of topology.
In the greedy routing scheme, a packet is forwarded to the next-hop neighbor node by unicast manner. In this method a sender node finds the position information of neighbor nodes, and selects the neighbor node which is closest to the destination node as the next hop node. AMAR [Brahmi (2009)] is a Movement Aware Greedy Forwarding (MAGF) based on the greedy forwarding scheme to select next-hop node towards the destination. AMAR scheme makes use of additional information about vehicle movement to select an appropriate packet’s next-hop that ensures the data delivery. This scheme is suitable for highly mobile vehicular ad hoc network and even it performs better in case of pure greedy forwarding failure. In AMAR every vehicle calculates its position, speed and direction by using the GPS or navigation system. Then after its significant role is to assign priority between neighbors while selecting a next-hop node for forwarding a packet. The basic idea of this approach is to compute a weighted score Wi which depends on three factors: the position, the speed, and the direction of vehicle nodes. This weighted score Wi can be computed by current packet forwarder for neighbor node I as follows:
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“ROMSG i.e. Receive on Most Stable Group-Path”  is an combination of most stable path with grouping of nodes depend on the velocity vector of nodes ; “AMAR i.e. Adaptive movement aware routing protocol”  is also an greedy based approach with the information of vehicle movement ; “EBGR i.e. Edge node based greedy routing protocol”  is also a greedy based approach with the node selection having limited transmission ; “B-MFR : Border-node based most forward within radius routing protocol”  is the extension of greedy based approach with the compatibility with high vehicular density environment; ”ARBR i.e. The Associativity-Based Routing”  is based on Signal Stability-Based Adaptive Routing protocol (SSR) for the route selection with “stronger” connectivity ,also define a metric known as degree of association stability ; “MORA i.e. Movement-Based Routing”  is designed to work on infrastructure-free ad-hoc networking scenario for C@C communication ; “VGPR i.e. Vertex-Based predictive Greedy Routing”  uses fixed infrastructure for packet forwarding through valid junction ; “MIBR: Mobile Infrastructure Based VANET Routing”  usus bus as basic element to choose rout because bus has two heterogeneous wireless system and find road density through bus line information ; “DTSG Dynamic Time-Stable Geocast Routing” is designed to work in sparse density network , it adjust itself dynamically according to network density and speed of vehicle ; “TO-GO i.e. Topology-assist Geo-Opportunistic Routing”  is the extension on greedy based protocol with better packet delivery and recovery forwarding ; “CBF i.e. Contention- Based Forwarding  is a geographic routing protocol because it not uses becon and save bandwidth.
Predictive directional greedy routing in vehicular ad hoc networks by J.Gong et al.  has two phases: Position first forwarding and direction first forwarding. In position first forwarding it follows greedy approach. The disadvantage of using this approach is that the node closest to the destination might be moving in the direction opposite to that of the source thereby paving way for possible loops in the route. To overcome this they propose direction first forwarding. In this approach the node closest to the destination and moving in the direction same as that of the source will be considered as the forwarding node. In order to reap the benefits of both position first and direction first forwarding they use weighted scores (Wi) calculated for the neighbouring nodes and the current node. In predictive routing weighted scores are calculated for both one and two hop neighbours. Of these neighbours the one with highest score is chosen as forwarder. This reduces the hop count as well as end to end delay.
Abstract: VANET is one of the networks most used for a vehicle. wVANET means Vehicular Ad-hoc Networks. It has received the considerable attention in recent years, a reason to its characteristics, which are different from MANET (Mobile Ad-hoc Networks) such as (i) rapid topology change, (ii) frequent link failure, (iii) high vehicle mobility. The VANETs network considers one drawback is network instability, it reduces the network efficiency. So the concept has overcome the drawback by using three algorithms namely, (a) Cluster- Based Life-Time Routing (CBLTR) protocol, (b) Intersection Dynamic VANET Routing (IDVR) protocol, (c) Control Overhead Reduction Algorithm (CORA). The first algorithm CBLTR protocols main goal is to increase the route stability and increase average throughput in a bidirectional segment scenario. The CH means Cluster Heads it is selected based on the maximum lifetime among all vehicles that are located within each cluster. Then, the second algorithm IDVR protocols main goal is to increase the route stability and increase average throughput and reduce the end-to-end delay in a grid topology. The CH is elected intersection because it classifies the cluster. The CH receives a Set of Candidate Shortest Routes (SCSR). In this SCSR is covers the desired destination from Software Defined Network (SDN). Then, the IDVR protocol selects the optimal route. The optimal route means the correct way and it is based on (i) its current location, (ii) destination location, (iii) the maximum of the minimum average throughput of SCSR. The third algorithm CORA protocols main aim is to reduce the controls the overhead messages in the clusters by developing a new mechanism to evaluate the optimal numbers of control overhead messages between the cluster members and the CH. It used the SUMO traffic generator simulators. SUMO means Simulation of Urban Mobility. The MATLAB (MATrix LABoratory) is to evaluate the performance of three protocols. The three protocols (CBLTR, IDVR, CORA)
Abstract: mobile sensor network can defined as a wireless sensor network in which the sensor nodes are mobile. The mobile sensor networks are much more versatile than the static sensor networks. In this paper location-based routing protocol called Partial-partitioned Greedy Algorithm (PPGA), for mobile sensor networks that consist of frequently moving sensors is proposed. The protocol uses the location information of sensors and the base station to assign a cost function to each sensor node, which is close to the Euclidean length of a sensor node’s shortest path to the base station. A packet is forwarded to the base station using greedy forwarding whenever possible. When a packet reaches sensor nodes near local minimums, where greedy forwarding will be impossible after a number of hops, the packet is forwarded following the high-cost to low-cost rule. Extensive simulations are used to compare the performance of PPGA with Greedy Perimeter Stateless Routing (GPSR) and Ad-hoc On-demand Distance Vector protocol (AODV) in mobile sensor networks. Experimental results show that PPGA achieves higher packet delivery ratio, collision rate, lower average delay and lower energy consumption.
This paper presents a deterministic greedy heuristic (GHI) to tackle the workforce scheduling and rout- ing problem (WSRP). We perform computational ex- periments using a data set consisting of 374 instances from four different WSRP scenarios. The proposed heuristic needs only two parameters. One to decide the order in which activities are processed. The other one to decide the order in which employees are con- sidered for the assignation. This tailored heuristic is able to tackle time-dependent activities constraints which arise when activities are related to each other. We run experiments to compare the results obtained by GHI against the results produced by a commercial mathematical programming solver. Our experiments also seek to identify the best settings for the two pa- rameters mentioned above.
Perimeter forwarding is used where greedy forwarding fails. It means when there is no next hop closest neighbour to the destination is available then perimeter forwarding is used. Perimeter forwarding uses nodes in the void regions to forward packets towards destination. The perimeter forwarding used the right hand rule. In right hand rule , the voids regions are exploited by traversing the path in counter clockwise direction in order to reach at specific destination. When a packet forward by source node, it forwarded in counter clockwise direction including destination node until it again reached at the source node. According to this rule each node involved to forward packet around the void region and each edge that is traversed are called perimeter. Edges may cross when right hand rule finds perimeter that are enclosed in the void by utilizing heuristic approach . Heuristic has some drawbacks besides it provides maximum reach ability to destination. The drawback is that it removes without consideration of those edges which are repeated and this may cause the network partitions. To avoid this drawback another strategy is adopted that is described below.
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K. Seada, A. Helmy, and R. Govindan et al.  offer an inclusive review on face routing protocol and its improvisation over geographical routing, it only uses location information about nodes to do routing and it provably guarantees message delivery in static connected plane graphs, Face routing is applied on a plane sub graph of the network graph. A specifics routing protocol provides a set of rules for each node to decide where to send a packet using only the local Information about its neighbors and the information in the packet header
The Initial proposals for geographic routing were simply based on greedy forwarding approaches. These algorithms even in a connected network did not guarantee a packet delivery and packet was being dropped. Since then several algorithms GPSR, GEAR and the GOAFR algorithms that are variations of compass routing have been proposed. In GPSR If the router position is known and also the destination is known the node will communicate the packet to the neighbour being closest to packet’s destination (Euclidean distance). As only neighbour and destination addresses are stored, rather than whole network, it is almost a stateless protocol. GEAR (Geographic and Energy Aware Routing) use energy aware neighbour selection for routing towards the target region.
applications, passive mobility driven routing solution is implemented. Location Based delay tolerant protocols provide promising solutions for wireless sensor based mesh networks. These protocols take advantage of the location information of nodes for their operation. This help store strict the process of flooding to a particular direction from where the information is to be sent. These save activating other nodes which will never come in the route. REFERENCES
A wireless ad hoc network is a decentralized type of wireless network consists of a collection of mobile nodes communicating with each other without using any central administration devices such as base stations or access points. In these networks, nodes can communicate directly with other nodes within its transmission range, otherwise, communication is done through intermediate nodes, and hence the node acts as both host and router[5, 28]. The fact that power and bandwidth are scarce resources in such networks of low powered wireless devices, requires more efficient routing protocols . There are a number of routing protocols proposed for Mobile Ad Hoc Networks (MANETs) which can be categorized into two different approaches: topology-based and position-based routing protocols [20,12]. In topology-based routing protocols packet forwarding is performed using link information that exists in networks. These protocols can be further divided into proactive (table driven), reactive (on demand) and hybrid approaches [25,22,15].
LAR  is an on-demand routing protocol that uses the last known position of the destination node and its velocity to limit the flooding of route requests toward the destination. Flooding is limited to an area between the source and a circle, calculated around the destination, with its centre at the last known position and a radius, which is determined by the node’s velocity. The main drawback of this protocol is scalability and latency associated with on-demand strategies.
The results can be used by many real-world applications such as the optimization of routing messages over a network and the orchestration of services across a distributed system such as provided by Cloud Computing environments and micro-services-oriented architectures. Besides it is also a future reference on the subject of Information Theory, Computational Complex Theory and Logarithmic utility in optimization routing, deployment, scheduling and planning. The research demonstrated that the proposed concepts have statistically significant results with better solution quality in tour planning. The model provides a new interpretation of entropy in problems encoded in Turing Machines and has the potential to change the traditional interpretation of the limits of Computing Theory.
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E-GyTAR is an enhanced version of GyTAR. It is designed for routing in city environment. It assume that vehicles in the discussing network have GPS devices, location services, digital maps, and IFTIS on board. Vehicles can get their own geographic positions though GPS devices, get the ultimate destination’s position though location service such as Grid Location Services (GLS) . Digital maps can provide the position information of the junctions in the city. IFTIS is the Infrastructure-free traffic information system used to calculate the real time vehicular density between adjacent junctions. As nodes in VANETs can always get sufficient power supply and processing capacity, these assumptions are reasonable.