A. Routing Protocols for Low-Power and LossyNetworksRPL is a proactive IPv6-based distance-vector routing proto- col. RPL can establish a Destination Oriented Directed Acyclic Graph (DODAG) at a high speed with the trickle algorithm . According to applications’ objectives, RPL uses different routing metrics to support LLNs applications. The root node serves as a transit point to bridge the DODAG with the IPv6 network. The formation of a DODAG is initiated by the root node that periodically originates DODAG Information Object (DIO). RPL is designed to optimize the routing support for multipoint-to-point (MP2P). RPL chooses the best next hop as the preferred parent to the root node given a particular objective function . Although RPL can support the routing for generic traffic pattern, RPL needs to pre-establish routes and can only route along pre-established DAGs for P2P com- munication. The source node has to send the packet upwards until it reaches the ancestor node of the destination node. Then the common ancestor node delivers the packet downwards towards the destination node. In non-storing mode of RPL, the common ancestor has to be the root node. Hence, packets need to travel through many lossy links, resulting in long end- to-end delay. Additionally, the root becomes a bottleneck when traffic load becomes heavy.
A key noticeable point here is the network perfo rmance in terms of Packet Delivery Ratio. While all compared algorithms achieve approximately similar results in the lossless scenario whatever is the value of k as illustrated in Fig. 13, the case is somewhat different when the network is experiencing losses and low redundancy factor values. Fig. 14 shows that Drizzle improves the PDR especially with lower values of k by up to 10% compared to other algorithm. The slightly better performance of Drizzle in terms of PDR in lossynetworks indicates the capacity of Drizzle in discovering more optimal routes with much less traffic overhead. The main reason behind this efficiency lies in the way Drizzle distributes the transmission of control messages through the network. Drizzle’s adaptive suppression mechanism, in addition to its slotting mechanism, ensures the fairness in the distribution of transmitted control messages. The fair distribution among nodes guarantees the optimal routes discovery for all the nodes and, thus, improving the packet delivery ratio. It is also clear from Fig. 15 and Fig. 16 that the superiority of Drizzle over Trickle’s variances in terms of PDR has been achieved under lowpower consumption rates in both networks types (i.e. lossy and lossless) regardless of the value of the redundancy factor. This is also can be attributed to the capacity of Drizzle to minimize the overhead and discovering the optimal routes affecting positively the power consumption. Pertaining to convergence time, Drizzle also converges faster than Trickle’s variances under different values of k , and whether the network is lossless or lossy as illustrated in Fig. 17 and Fig. 18 respectively. This also is attributed to the facts explained previously regarding removing the listen-only period which contributes into enhancing the convergence time.
(i.e., impose hardware constraints) the Tmote Sky platform, an MSP430-based board with an ultra-lowpower IEEE 802.15.4 compliant CC2420 radio chip. The Unit Disk Graph Radio Medium (UDGM) with different loss rates was used in order to simulate the radio propagation in lossless and lossynetworks. The CSMA/CA protocol is used as at the MAC layer, while the ContikiMac is used at the radio duty cycling (RDC) layer. The Minimum Rank with Hysteresis Objective Function (MRHOF) with ETX metric is selected for calculating the ranks of nodes and building the DODAG due to its efficiency in characterizing the quality of links. At the application layer, a periodic data collection application where each node sends to the sink one packet every 60 seconds (the time of packet transmission is randomly chosen within the 60 seconds period) is simulated. Both uniform and random topologies where nodes are spread in a square area of 200 x 200m dimensions are considered in the simulations experiments. The border router (sink) is placed in the middle of the network. For each scenario, ten simulation experiments with different seeds are run in order to get statistically solid results. The graphs show the average (mean) values of the results and the error bars at the 95% confidence interval of the mean. The simulation time is selected to be 20 virtual minutes for each experiment. For clarity, other simulation parameters are provided in Table 4-II which covers a wide range of scenarios that could be deployed in real home-automation environments.
Abstract: RPL is the IPv6 routingprotocol for low-power and lossynetworks, standardized by IETF in 2012 as RFC6550. Specifically, RPL is designed to be a simple and inter-operable networking protocol for resource-constrained devices in industrial, home, and urban environments, intended to support the vision of the Internet of Things with thousands of devices interconnected through multihop mesh networks. More than six-years have passed since the standardization of RPL, and we believe that it is time to examine and understand its current state. In this paper, we review the history of research efforts in RPL; what aspects have been (and have not been) investigated and evaluated, how they have been studied, what was (and was not) implemented, and what remains for future investigation. . We reviewed over 97  RPL-related academic research papers published by major academic publishers and present a topic-oriented survey for these research efforts. Our survey shows that only 40.2% of the papers evaluate RPL through experiments using implementations on real embedded devices, ContikiOS and TinyOS are the two most popular implementations (92.3%), and TelosB was the most frequently used hardware platform (69%) on testbeds that have average and median size of 49.4 and 30.5 nodes, respectively. Furthermore, unfortunately, despite it being approximately four years since its initial standardization, we are yet to see wide adoption of RPL as part of real-world systems and applications. We present our observations on the reasons behind this and suggest directions on which RPL should evolve. The RPLroutingprotocol published in RFC 6550 was designed for efficient and reliable data collection in low-power and lossynetworks. Specifically, it constructs a Destination Oriented Directed Acyclic Graph (DODAG) for data forwarding. However, due to the uneven deployment of sensor nodes in large areas, and the heterogeneous traffic patterns in the network, some sensor nodes may have much heavier workload in terms of packets forwarded than others. Such unbalanced workload distribution will result in these sensor nodes quickly exhausting their energy, and therefore shorten the overall network lifetime. In this paper, we propose a load balanced routingprotocolbased on the RPLprotocol, named LB-RPL, to achieve balanced workload distribution in the network. Targeted at the low-power and lossy network environments, LB-RPL detects workload imbalance in a distributed and non- intrusive fashion. In addition, it optimizes the data forwarding path by jointly considering both workload distribution and link-layer communication qualities. We demonstrate the performance superiority of our LB-RPLprotocol over original RPL through extensive simulations.
Abstract —LowPower and LossyNetworks (LLNs) represent one of the interesting research areas in recent years. The IETF ROLL and 6LoWPAN working groups have developed new IP based protocols for LLNs such as the RPLroutingprotocol. In LLNs e.g. 6LoWPANs, heavy data traffic causes congestion which significantly degrades network performance. In this paper, we explore the impact of congestion on 6LoWPAN networks where an extensive analysis is carried out with different scenarios and parameters. Analysis results show that when congestion occurs, the majority of packets are lost due to buffer overflow as compared to channel loss. Also, we found that when the application payload length is increased since IPv6 packets are fragmented, the reassembly timeout parameter value has a significant effect on network performance. Thus, it is important to consider buffer occupancy and the reassembly timeout parameter in protocol design, e.g. RPL, to improve network performance when congestion does occur.
Abstract—With growing needs to better understand our en- vironments, the Internet-of-Things (IoT) is gaining importance among information and communication technologies. IoT will en- able billions of intelligent devices and networks, such as wireless sensor networks (WSNs), to be connected and integrated with computer networks. In order to support large scale networks, IETF has defined the RoutingProtocol for Lowpower and LossyNetworks (RPL) to facilitate the multi-hop connectivity. In this paper, we provide an in-depth review of current research activities. Specifically, the large scale simulation development and performance evaluation under various objective functions and routing metrics are pioneering works in RPL study. The results are expected to serve as a reference for evaluating the effectiveness of routing solutions in large scale IoT use cases.
Networks connecting smart objects and sensor nodes, often operate in highly variable link quality conditions. The link speed for these networks are often limited to a few tens of Kbps at maximum. The interconnection of various smart objects, for the link quality connecting them and due to the constraint on size and capacity of the processing units, are classified as LowPower and LossyNetworks (LLNs). LLNs are emerging as a new deployment scenario in many environments, clearly those related to smart home, building or industrial automation, communication among AMI meters in a smart grid and for the most part of the envisioned Internet of Things (IoT). The challenges in these networks include very low device power and memory, highly varying link quality, frequent link outage, etc. Requirements for these deployments such as in smart home, building or industrial automation, communication among AMI (Advanced Metering Infrastructure) meters in a smart grid, etc., relate to delay bound, scalability, strict time bounds on protocol convergence after any change in topology (5), etc. For instance, RFC 5673 (6) requires bounded and guaranteed end-to-end delay for routing in an industrial deployment, while RFC 5548 (7) mandates scalability in terms of protocol performance for a network of size ranging from 10 2 to 10 4 nodes. Link state routing protocols, such as OSPF(8), OLSR (9), IS-IS, and OLSRv2 (10) tend to flood the network with link updates. Since links in LLNs suffer from severe temporal variation and frequent outage, these protocols fail to keep a low control cost overhead (11). Classical distance vector protocols used in the Internet, such as EIGRP (designed by CISCO, (12)), AODV (13), etc., fail to provide quick recovery from link churn, and frequent topology updates.
As it can be seen in Figure 4.1, using low values for the redundancy constant yields high network convergence time in all network size and density scenarios considered. This behavior is mainly due to the message suppression mechanism in the Trickle algorithm. When the root node transmits its first DIO message, the neighbors of the root that receive this DIO message schedule themselves to send their own first DIO message. However, when k is set to a low value, only a few neighbors of the root will be allowed to transmit their DIO message. If the rest of root neighbors (if any) hear a number of DIO messages greater than or equal to k, they will suppress their DIO message transmission in the current interval. As a result, nodes that are only neighbors of the latter root neighbors have to wait for subsequent intervals to have the opportunity to receive their first DIO message. The same phenomenon happens as DIO messages propagate through the network, which finally leads to high network convergence time.
Packet Delivery Ratio detected in the simulation is shown in figure 2. The results are compared for standard RPL and proposed with memory bank. The PDR is 98.01 to 100.00% when memory bank is enabled which is good. On the other hand, PDR for standard RPL has low value (72.83% to 94.41%).
Typically, Wireless Sensor Networks (WSNs), Internet of Things (IoT), and Cyber-Physical Systems (CPS) all require low-power and lossynetworks (LLNs) for some or all of their operation. These networks are characterized as LLNs because nodes in such networks possess limited resources and often operate in harsh communication environments, resulting in transmission losses over wireless links. Communication and computing devices are increasingly being embedded in objects and structures to enable networked sensing and actuation functions. This is resulting in sophisticated CPS, such as power generation and distribution networks, assisted living, traffic control, home automation, safety systems, autonomous vehicles, and distributed robotics. Unlike the pure sensing focus of traditional WSNs, CPS require distributed decision making, control, and actuation. Hence, lowpower devices need to communicate not only with a central gateway, but also need to directly communicate
Abstract: In an industrial IoT applications there will be more demand for adaptability to network dynamics and throughput support in an IoT networks, hence RPL(routingprotocol for Lowpowerlossynetworks) LLN which supports only for static environments, hence in this paper we discuss the disadvantages of RPL on adaptability to network dynamics and throughput we proposed BRPL(Back pressure routingprotocol for LowpowerLossy Network) LLN to overcome the RPL disadvantages on throughput and adaptability to network dynamics in the industrial IoT applications. An IoT protocols are mainly used in smart cities, public water system, power grid and vehicle traffic control etc in this paper we also assure authenticity for these devices by using light weight mutual authentication method. Performance results are shown on simulation using Cooja simulation software for IoT applications.
ABSTRACT: Wireless sensor networks (WSNs) bring significant preferences over conventional changes in todayˆas applications, for example, natural observing, homeland security, and human services. A wireless sensor network (WSN) has critical applications, for example, remote natural observing and goal tracking. This has been empowered by the availability, especially in late years, of sensors that are smaller, less expensive, and intense. The Internet Engineering Task Force (IETF) immediately perceived the need to structure a new Working Group to standardize an Ipv6-based directing answer for IP strong article networks, which prompted the arrangement of another Working Group called ROLL (Routing over Lowpower and Lossy) networks. We have additionally proposed another technique to expand the dependability of the network.
Abstract— The IETF ROLL WG is currently in the final steps of the specification of RPL, a new routingprotocol for lowpower and lossynetworks (e.g. wireless sensor networks). RPL may use layer two- and layer three-based mechanisms for neighbor reachability maintenance. Since layer two mechanisms may not always be available, RPL relies by default on the 6LoWPAN Neighbor Discovery, a version of the IPv6 Neighbor Discovery which is optimized for LLNs. This paper provides an analysis of the impact of various RPL and 6LoWPAN Neighbor Discovery parameter settings on the link availability and end-to-end path availability, and the related message overhead. Results show that careful tuning of the relevant parameters is critical for obtaining good network performance.
A WSN is a specialized wireless network made up of a large number of sensors and at least one base station. The sensor nodes are small devices that consist of four basic components 1) sensing subsystem, 2) processing subsystem, 3) wireless communication subsystem 4) energy supply subsystem . The sensor nodes have limited battery power, communication range and memory etc. . WSN are characterized with denser levels of sensor node deployment, higher unreliability of sensor nodes and several power, computation and memory constraints . Due to severe energy constraints of large number of densely deployed sensor nodes, it requires a suite of network protocols to implement various network control and management functions such as synchronization, node localization and network security . With the advances in micro-electro-mechanical system technologies, embedding system technology and wireless communication with lowpower consumption, it is now possible to produce micro wireless sensors for sensing, wireless communication and information processing . These inexpensive and power- efficient sensor nodes works together to form a network for monitoring the target region. Through the cooperation of sensor nodes, the WSNs collect and send various kinds of message about the monitored environment (e.g. temperature, humidity, etc.) to the sink node, which processes the information and reports it to the user. Sensor networks have a
The major failure in flexible pavements is fatigue cracking This fatigue leads to formation of cracks, pot holes and undulations on the pavements. In flexible pavements load will be distributed into lower layers in decreasing order. EPDM rubber consists of properties like tensile strength, abrasion, resistance to temperature. Wireless communication technology in WSN contains two types of communication methodologies i.e., Wireless transportation based communication model and wireless transportation-less network communication model . Wireless transportation based communication model contains wireless movable nodes and permanent nodes. The wireless movable nodes exchange the information data with fixed nodes through pre- established transportation . The wireless transportation- less system communication model is nothing but wireless MANETs which contains movable wireless nodes spread in the radio communication region and they communicate with each other through relying on in-between node i.e., with lacking transportation and thus WSN has to perform as a peer to peer network. However, contact among communicating nodes is very challenging due to the features of WSN. Moreover, wireless movable node working in a network has restricted with power batteries and it is not probable to recharge the energy of the batteries during the given task. Applications of WSN mainly include military, healthcare, natural, household & commercial areas as well as disaster recovery. Due to its variety of features, WSN is paying attention by the majority of the researchers and hence the group of routing protocols has been intended based on considering diverse parameters. One of those routing protocols is “energy aware routingprotocolbased on the reactive status of movable nodes” [3
RPL is a proactive routingprotocol for LLNs as de- fined in RFC6550 (Request for Comments) (Shelby et al., 2012), based on distance vectors and operate on IEEE 802.15.4 (Molisch et al., 2004), which is stan- dardized for constrained and IP-based environment, such as 6LoWPAN networks (IPv6 Lowpower Wire- less Personal Area Networks), and it is known as the standard routingprotocol for IoT based LLNs such as WSNs (Gaddour and Koubˆaa, 2012). In RPL, the net- work topology organized as DAG (Directed Acyclic Graph), which is similar to the tree, while in DAG nodes can associate to multiple parents not like tree. Specifically, nodes are organized as DODAGs (Des- tination Oriented DAGs) (Winter et al., 2012), where RPL assigned for each node in the network a rank, which represents the individual position of that node (Ghaleb et al., 2018). In fact, it increases monoton- ically while moving away from the root nodes (sink nodes or DODAG root) towards the leaf nodes, then inversely decreases from root nodes to leaf nodes. Whilst, data is transmitted upward to root nodes or downward to leaf nodes (Thubert, 2012).
This section explores the many existing energyefficient techniques proposed to address the energy efficiency, network stability, residual energy and network lifetime etc. In W. Heinzelman  proposed a lowenergyefficient adaptive clustering routing algorithm to address the above issues. This routing mechanism is a probabilistic approach which the cluster heads are selected based on the random number between 0 to 1. The main drawback of this algorithm is based on the random number and other parameters are not considered so the network performance is very poor. S. Lindsey etal., has introduced the powerefficient information gathering routingprotocol that forms chain of nodes using greedy algorithms . The sensing nodes near to the base stations selected as leader to forward the collected data. This will improve the energy consumption. This will increase the transmission end to end delays due to long chain. The more amount of the energy is depleted because of the resulting in the network partitioning. In H. Farman, proposed an optimum cluster head selection algorithm  is proposed to maximize the network lifetime and to distribute evenly the entire nodes across the networks. This routingprotocol is based on merge and split techniques. The whole network is divided into certain zones and the total number of nodes in each zone are identified. The node density is less than the threshold, so that the nodes are merged with the all the zone neighboring nodes. The major disadvantages of this routingprotocol are to sudden ————————————————
Figure 9 (a) and (b) show the delivery rate and bits/unit energy for distance-based blacklisting. The optimum black- listing thresholds are within the transitional region which conforms with our analysis. The delivery rate is low at low thresholds, because of forwarding on low reception rate links that cannot deliver the packet even with 10 retransmissions. At high thresholds the delivery rate decreases again because of the increase in greedy disconnections that happen when all nodes closer to the destination are blacklisted. The black- listing threshold has a trade-off between the quality of the link, the number of hops in the path between source and destination, and the greedy connectivity. Using low thresh- olds increases the possibility of forwarding on high-loss links causing the packet to be dropped. On the other hand, higher thresholds reduce the connectivity and increase the chances of greedy failures. Greedy disconnection is an important factor that was not captured in our analytical model that assumed very high density. As the density gets lower, the optimum threshold shifts to the left, since at lower densi- ties the possibility of greedy disconnections is higher. The bits/unit energy also decreases at higher thresholds because of the wasted overhead of transmitting packets over multiple hops before being dropped due to greedy disconnections, in addition to the distance-hop energy trade-off. It is instruc- tive to notice that at low densities, increasing the threshold does not cause much improvement over the original greedy, which indicates that forwarding based on distance only is very limited in improving the performance.
and fast growing fields in the scientific world. This has brought about developing low cost, low-power and multi-function sensor nodes. However, the major fact that sensor nodes run out of energy quickly has been an issue and many energyefficientrouting protocols have been proposed to solve this problem and preserve the longetivity of the network. This is the reason why routing techniques in wireless sensor network focus mainly on the accomplishment of power conservation. Most of the recent publications have shown so many protocols mainly designed to minimize energy consumption in sensor networks. This dissertation work proposes a hierarchical routing technique which shows energy efficiency. Our technique selects cluster head with highest residual energy in each communication round of transmission and also takes into account, the shortest distance to the base station from the cluster heads. Simulation results show that hierarchical routing technique with different level of hierarchy prolongs the lifetime of the network compared to other clustering scheme and the energy residual mean value after some communication rounds of simulation increases significantly.
A sensor transmits to its local neighbours in the data fusion phase rather than sending directly to its CH as in the case of LEACH. In PEGASIS routingprotocol, in the construction phase, it has been assumed that all the sensors have knowledge about the entire topology about the network, particularly, the location of the sensors and use a greedy approach. In case of sensor failure due to low battery power, the chain is constructed again using the same greedy approach by isolating the failed sensor from the network. In each round, a randomly selected sensor node from the chain will transmit the collected data to the BS, thus reducing the per round energy utilization as compared to LEACH. Simulation results showed that PEGASIS will improve the life span of the network to a factor of two as compared to the life span of the network under the LEACH protocol. This improvement in the performance is achieved by eliminating the overheads due to dynamic cluster formation in LEACH and by reducing the number of transmissions and receptions by using data aggregation. Although the overheads due to cluster formation are avoided, PEGASIS still requires dynamic changes in network topology since a sensor node must have information about remaining energy status of its neighbours so as to identify the route to forward its data. Such topological adjustments can introduce high overheads mainly in highly utilized networks.