Poor Link Quality Research Articles

Mobility-Assisted Digital Twin Network Optimization over Industrial Internet of Things

Many real-world networks for the Industrial Internet of Things (IIoT) have diverse connectivity characteristics and real-time constraints imposed by industrial processing. In the context of digital twin networks (DTNs), a large number of IIoT devices may access the network and have a tremendous volume of data. A crucial element of these IIoT devices is mobility, which cannot be effectively solved because the number of IIoT devices connected is extremely large. IIoT devices in DTNs suffer from poor data transmission and link quality because of their mobility. In this paper, device-to-device (D2D) communication-based mobility-assisted digital twin networks are proposed, where edge computing is introduced to design an efficient mapping between the physical and virtual space. Then, we propose the architecture of data transmission for the D2D network to maximize the data rate for reliable connectivity among multiple mobile nodes based on IIoT. A Markov decision process (MDP) is formulated to maximize the data rate for multiple mobile nodes while maintaining the D2D communication link quality. The simulation results demonstrate the superiority of the proposed scheme over other comparable models.

Topology Perception and Relative Positioning of UAV Swarm Formation Based on Low-Rank Optimization

In a satellite-denied environment, a swarm of drones is capable of achieving relative positioning and navigation by leveraging the high-precision ranging capabilities of the inter-drone data link. However, because of factors such as high drone mobility, complex and time-varying channel environments, electromagnetic interference, and poor communication link quality, distance errors and even missing distance values between some nodes are inevitable. To address these issues, this paper proposes a low-rank optimization algorithm based on the eigenvalue scaling of the distance matrix. By gradually limiting the eigenvalues of the observed distance matrix, the algorithm reduces the rank of the matrix, bringing the observed distance matrix closer to the true value without errors or missing data. This process filters out distance errors, estimates and completes missing distance elements, and ensures high-precision calculations for subsequent topology perception and relative positioning. Simulation experiments demonstrate that the algorithm exhibits significant error filtering and missing element completion capabilities. Using the F-norm metric to measure the relative deviation from the true value, the algorithm can optimize the relative deviation of the observed distance matrix from 11.18% to 0.25%. Simultaneously, it reduces the relative positioning error from 518.05 m to 35.24 m, achieving robust topology perception and relative positioning for the drone swarm formation.

Cooperative communication and relay node selection algorithm based on SWIPT

The development of nanotechnology and the emergence of new materials such as graphene make wireless nanosensor networks (WNSN) based on electromagnetic communication possible. Due to the energy, computing power and transmission distance carried by nano-nodes are extremely limited, nanonetworks are usually ultra-dense, so nanonetworks are usually cluster-managed. Aiming at the problem of data transmission failure caused by poor link quality or insufficient energy in inter-cluster transmission, a relay node selection algorithm (RNS-AHP) based on simultaneous wireless information and power transfer (SWIPT) and analytics hierarchy process (AHP) is proposed. The algorithm is based on the SWIPT cooperative communication model, and uses the AHP to calculate the weights of four factors: the distance of the relay node, the channel capacity, the remaining energy of the relay node and the signal-to-noise ratio of the relay node receiver in the process of relay node selection. According to the weight, the weight of the candidate relay node is obtained as the selection criterion of the relay node. The simulation results show that the RNS-AHP algorithm can improve the average throughput, the success rate of data packet transmission and the energy efficiency.

Wireless Powered Cooperative NOMA With Alamouti Coding and Selection Relaying

Nonorthogonal multiple access (NOMA) can facilitate simultaneous data transmissions towards multiple users by using the superposition coding and successive interference cancelation techniques, which can greatly improve the spectrum efficiency. Cooperative relaying and space-time coding can promisingly improve the communication robustness of poor-quality links by achieving the space-diversity gain. Energy harvesting (EH) can prolong the lifetime of energy-limited terminals and make them work continuously. In order to enhance the spectrum efficiency as well as the communication quality, we enable a cluster of EH relays to assist the data transmissions from a base station (BS) to two far-users by using the Alamouti coding based cooperative NOMA strategy. The relays are capable of harvesting wireless energy from a power beacon as well as BS by using time-switching or power-splitting method. According to the energy status and the data decoding status, one relay is selected in a distributed manner according to either Max-min, Max-sum, or Random criterion. We analyze the transmission success probability and the system throughput performance. Extensive simulations are performed to compare the performance of different EH-based space-time coded cooperative NOMA with various relay selection schemes as well as the counterpart orthogonal transmission schemes.

A Resource Allocation Policy for Downlink Communication in Distributed IRS Aided Multiple-Input Single-Output Systems

As a technology for 6G wireless communications, Intelligent Reflecting Surfaces (IRSs) are considered as a promising solution to boost the network capacity, spectrum and coverage in multiusers’ downlink communication systems. The users in blockage and cell edge areas can utilize this technology for data transfer purpose. In this paper, a machine learning-based policy optimization for downlink communication in distributed IRS aided multiple-input single-output (MISO) systems is proposed. Three categories of users are considered, namely, users who can utilize only the direct links, blockage area users who can utilize only the IRS links, and cell edge or poor link quality of users who can utilize both the direct and IRS links. The sum rate maximization problem is formulated to derive the optimal policy (i.e. communication link, IRS selection, power allocation and reflection coefficients) for those users, considering the IRS selection, link quality, power allocation and IRS reflection constraints. The proposed methods to achieve the optimal policy include reinforcement learning-based model with binary decision tree-based user categories, maximum posterior probability-based IRS selection, fractional programming method-based power and IRS coefficient allocation, and value function-based policy optimization. Through simulations, the sum data rate and energy efficiency performances of different categories of users are obtained and discussed.

Optimal relaying nodes selection for IEEE 802.15.6-based two-hop star topology WBAN

Wireless body area networks (WBANs) are a promising communication technology that supports various types of medical and non-medical applications with heterogeneous requirements. Generally, IEEE 802.15.6-based WBAN use one-hop star topology to establish direct communication between the hub and body sensors. The standard also supports two-hop star topology to establish communication where direct transmission between the hub and sensor nodes is not possible due to poor link quality caused by body shadowing. Data transmission reliability and network lifetime are key factors in WBANs. Therefore, we propose an optimal relaying nodes selection mechanism as an alternative to the two-hop star topology extension of IEEE 802.15.6 to enhance the packet delivery ratio, network lifetime, and throughput. In this work, while choosing a relaying node for a disconnected node, we consider the fitness value of all the relaying capable nodes and select the fittest node as relaying node. We calculate the fitness value of all the relaying capable nodes based on their link quality, residual energy, and current traffic load using the multi-criteria decision making method “Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)”, which was originally developed by Ching-Lai Hwang and Yoon in 1981. We use the Mamdani fuzzy inference system (FIS) to calculate the link quality index of relaying capable nodes based on various link quality parameters such as Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), and Packet Error Rate (PER). Moreover, we also designed a schedule slot allocation mechanism for all the sensor nodes based on their packet generation rate (PGR). The proposed scheme significantly improved packet delivery ratio, throughput, and network lifetime compared to the IEEE 802.15.6 standard’s two-hop extension of star topology and other relay selection mechanisms. The simulation result of the proposed mechanism shows that the packet delivery ratio and network lifetime increase by (5–30)% and 50% respectively, compared to the IEEE 802.15.6 two-hop star topology.

Adaptive link quality routing protocol for UASNs with double forwarding modes

Underwater Acoustic Sensor Networks (UASNs) has been used in various fields. However, UASNs is characterized by long delay, poor and time-varying link quality and unequal node distribution. Opportunistic routing (OR) uses broadcast forwarding to overcome poor link quality, but it increases delay compared to unicast forwarding, and most OR protocols cannot overcome routing voids. This paper proposes an Adaptive Link Quality Routing Protocol (ALQR) with double forwarding modes to solve these problems. This protocol innovatively selects the forwarding mode adaptively by link quality and uses distance vectors to construct candidate set to avoid routing voids and long detour. First, a new query algorithm is proposed to obtain the residual energy of neighbors and establish all distance vectors between sensor nodes and sink nodes, reducing the overhead of control packets. Distance vectors guide senders to determine the candidate set and avoid routing voids and long detours based on the shortest path principle. Second, an improved triangle metric is introduced to measure the accurate link quality. Based on this, a novel forwarding strategy combining unicast and broadcast is proposed. The unicast forwarding mode directedly select next hop based on route cost, which shortens the delay. Moreover, a new priority-based waiting-competition mechanism is proposed to determine the forwarding nodes in the broadcast forwarding mode, reducing redundant packets. The simulation results indicate that ALQR outperforms other routing protocols in terms of end-to-end delay and energy consumption when sacrificing little PDR. Specifically, delay is reduced by an average of 37.51% and 19.54% and energy consumption is reduced by an average of 35.01% and 16.41%, compared to DBR and DVOR.

Traffic priority based delay aware scheduling for co-located WBANs

Wireless Body Area Network (WBAN) is a promising solution that has gained significant attraction in the human health monitoring system. Body sensors measure the physiological parameters of the human body and send the data to the access point via coordinator or local processing unit (e.g., smartphones, PDA) for further analysis. Data transmission from the local processing unit to the access point, known as inter-WBAN transmission, is challenging since many problems such as interference, poor link quality, and scarcity of resources can arise. As a result, data may be lost, or transmission may be delayed, which turn can endanger the user’s health. The situation could be worse when the user suffers from critical diseases and has to coordinate with other WBANs co-located in the same vicinity. This paper focuses on dynamic slot scheduling and topology handling of WBAN, whose neighborhood is constantly changing due to its mobility. To guarantee the performance of different WBAN’s criticality; we propose Critically Aware Threshold Sensitive (CATS) scheduling algorithm for coexisting WBANs data transmission. CATS treats all the nodes in the same WBAN as a whole and allocates the same slot to nodes of the WBAN so that the data transmission delay of a critical WBAN can be minimized. The proposed method categories and schedules the WBANs based on the sensor data priority and WBAN’s criticality index. Furthermore, we have considered for categorizing the WBANs into various groups. In this work, the network performance is evaluated using these threshold values. Simulations are conducted to evaluate the performance of the proposed CATS against the existing scheduling algorithms with respect to various performance metrics. Simulation results show that the proposed method has lower transmission delay and higher spatial reuse factor.

An Opportunistic Network Routing Protocol for Maritime Voluntary Observation Ships

In order to solve the issue of data transmission for maritime voluntary observation ships, an opportunistic network routing protocol (MVOS-RP) is proposed for the complicated maritime environment. The proposed protocol transmits data through the encounter between ships. A connected graph is constructed with the sending node as the vertex, and then the selection of the relay node is divided into three scenes by traversing the graph through the Breadth First Search (BFS) algorithm. We also use the ship’s Automatic identification System (AIS) information, such as the estimated time of arrival and the route of ships, to select the appropriate relay node. MVOS-RP protocol solves the transmission issue of opportunistic network in the environment of sparse nodes and poor communication link quality. Simulation is performed on Opportunistic Network Environment (ONE) platform. The results show that, compared with other protocols, the proposed protocol has a better balance in terms of data delivery rate, average latency and routing overhead. It is a reliable and efficient opportunistic network routing protocol.

Estimating and predicting link quality in wireless IoT networks

The use of poor-quality links in Internet of Things (IoT) networks leads to a bad quality of experience (QoE) with long delivery delays, low reliability, short lifetime of battery-operated nodes, to name but a few. In addition, network resources, such as bandwidth and node energy, are wasted by retransmissions. An accurate estimation of link quality will enable the network to better select the links used for data gathering. Hence, the number of retransmissions needed to achieve the required end-to-end reliability is decreased, leading to shorter end-to-end delivery times, a higher network throughput and an increased network lifetime. In this paper, different link quality estimators are reviewed with a more particular focus on those based on Received Signal Strength Indicator (RSSI) and Packet Delivery Ratio (PDR). We propose to go even further than link quality estimation with link quality prediction. The expected benefit of using link quality prediction is to anticipate link breakages and route changes before loosing packets. It should result in a better QoE provided by the network. For that purpose, we evaluate the performance of four machine learning techniques (i.e. Linear Support Vector Machine, Logistic Regression, Support Vector Machine and Random Forest) working on the traces collected from a real IoT network. They are compared in terms of per-class metrics as well as global metrics. In addition, issues dealing with the deployment of such machine learning techniques in IoT networks with limited resources and energy are presented.

A Malicious Node Identification Strategy with Environmental Parameters Optimization in Wireless Sensor Network

In the wireless sensor network (WSN), nodes show a low forwarding rate under a poor-quality links environment and a resource-constrained state. The malicious nodes imitate this forwarding behavior, which can selectively forward date, eavesdropping, or discarding important dates. The traditional reputation model is challenging to identify with this kind of sub-attack nodes. To address these problems, a malicious node identification strategy based on time reputation model and environmental parameters optimization (TRM-EPO) is proposed in the WSN. First of all, the comprehensive reputation is calculated according to the direct reputation and the recommended indirect reputation. The environmental parameters matrix is based on nodes’ running state, taking into account nodes’ energy, data volume, number of adjacent nodes, and node sparsity. Besides, according to the environmental parameters matrix, and the recorded comprehensive reputation matrix, the next cycle’s trust can be predicted. Finally, a similarity of the actual reputation and predicted trust matrix is proposed to compare with an adaptive threshold to identify malicious nodes. The experimental results demonstrate that the proposed strategy improves sensor nodes’ security and reliability in a complex environment. Moreover, compared to comparison algorithms, the TRM-EPO improves the recognition rate above 1% and reduces the false-positive rate by more than 1%.

State-of-Art and Open Issues of Cross-Layer Design and QOS Routing in Internet of Vehicles

VANET (Vehicular Ad Hoc Network) is a significant term in ITS (intelligent transportation systems). VANETs are also mentioned as ITN (intelligent transportation Networks), which are used to enhance road safety in growing technology. The connectivity of nodes is a challenging one because of its high mobility and the sparse network connectivity must be handled properly during its initial deployment of a VANET for avoiding accidents. Quality of service (QoS) in VANET becomes a significant term because of its increasing dare about unique features, like poor link quality, high mobility, and inadequate transporting distance. Routing is the foremost issue in the wireless ad hoc network, which is used to transmit data packets significantly. This paper provides a crucial review of the classification of existing QoS routing protocols, cross-layer design approach and classification, and various performance parameters used in QoS routing protocols. The corresponding cross-layer protocols are overviewed, followed by the major techniques in cross-layer protocol design. Moreover, VANET is presented with many exclusive networking research challenges in precise areas such as security, QoS, mobility, effective channel utilization, and scalability. Finally, the paper concluded by various comparison discussion, issues, and challenges of several routing protocols for VANET. No. of publications over the period from 2010 to 2019 in various scientific sources also showed in this review. This survey provided the technical direction for researchers on routing protocols for VANET using QoS.

Crowdsensing-Based Cross-Operator Switch in Rail Transit Systems

Rail transit systems are important parts of modern cities such as the urban subways and intercity trains. During the travelling time, many passengers access the networks via cellular communications for working or entertainment. However, intermittent connection of LTE signals usually decreases the user experiences, especially due to the poor LTE link quality of underground subways. On the other hand, we observe that there are usually multiple cellular operators covering a city. For example, AT&T and T-Mobile in American cities, China Mobile and China Unicom in Chinese cities. Based on the different eNodeB distributions of different operators, we propose to study a new problem cross-operator switch . Unlike the handover between eNodeBs from one operator, cross-operator switch can switch to an eNodeB belonging to other operators. Although current mobile phones support dual SIM cards and Android supports the quick swap between two cards, it is still challenging to determine which eNodeB to switch because of no information exchange between different operators. To address this challenge, we propose a robust Crowdsensing based Switch (CrowdSwitch) solution dedicated for rail transit systems. CrowdSwitch utilizes mass mobile devices of passengers to sense and collect LTE signals along subway tracks, builds the signal heat map (S-Map) in the cloud servers, and finally recommends the optimal switch taking advantages of the known tracks. We implement CrowdSwitch in off-the-shelf mobile phones and conduct extensive experiments on Shanghai Metro. The results show that CrowdSwitch can depict the accurate LTE distribution and always recommend the optimal operator for users to connect.

Modelling an Adaptive Cluster Head Positioning Based Map Reducing Strategy for Data Transmission in Medical IoT

Internet-of-things (IoT) based health monitoring systems have turned out to be an interesting topic to enhance quality of health care services. Moreover, there is no advanced IoT based continuous monitoring of glucose systems in real time and some prevailing techniques have numerous limitations. Here, a continuous and invasive glucose monitoring system for transmitting the condition of individuals simultaneously utilizing IoT is modelled and a general system architectural design for processing back end systems to provide body temperature, real time glucose and contextual data in human readable and graphical forms to the physicians or patients is anticipated. As well, a protocol designs for monitoring the continuous data from IoT devices in order to overcome the short comings of existing methods is provided. The design of an energy efficient routing algorithm is a hot topic in the research of IoT based Data mining. Cluster Heads (CH) form backbone of inter-cluster communication. The selection of reliable and efficient cluster head is another important issue. In most of the clustering process, failure of CH occurs due to energy depletion and if the distance between sink and CH is more, it ultimately leads to failure in transmission. During transmission, nodes may fail that means sudden energy loss or node gets out of coverage. Due to relaying high data traffic, some of nodes quickly exhaust their energy and increase the risk of node failure. As a baseline to node failure, data packet loss also occurs in a CH due to congestion and poor link quality. Hence, one of the most crucial feature in designing a protocol is to minimize energy consumption for betterment of network functioning. Here, a clustering routing protocol based on data mining techniques is applied for sensor nodes in medical field called Adaptive Positioning of Cluster Head based Map reducing (APCH-MR) is proposed. Routing table based code book is generated for privacy concern, in which the process of mapping and reducing the data for dissemination is performed. The simulated outcomes depicts that the total number of packets transmitted in round 500 is 11200, total number of dead nodes during round 500 is 58, and time consumed by nodes at 500 rounds is 0.3751s respectively. The proposed method shows better trade off in contrast to conventional techniques.

Modelling an Adaptive Cluster Head Positioning Based Map Reducing Strategy for Data Transmission in Medical IoT

Internet-of-things (IoT) based health monitoring systems have turned out to be an interesting topic to enhance quality of health care services. Moreover, there is no advanced IoT based continuous monitoring of glucose systems in real time and some prevailing techniques have numerous limitations. Here, a continuous and invasive glucose monitoring system for transmitting the condition of individuals simultaneously utilizing IoT is modelled and a general system architectural design for processing back end systems to provide body temperature, real time glucose and contextual data in human readable and graphical forms to the physicians or patients is anticipated. As well, a protocol designs for monitoring the continuous data from IoT devices in order to overcome the short comings of existing methods is provided. The design of an energy efficient routing algorithm is a hot topic in the research of IoT based Data mining. Cluster Heads (CH) form backbone of inter-cluster communication. The selection of reliable and efficient cluster head is another important issue. In most of the clustering process, failure of CH occurs due to energy depletion and if the distance between sink and CH is more, it ultimately leads to failure in transmission. During transmission, nodes may fail that means sudden energy loss or node gets out of coverage. Due to relaying high data traffic, some of nodes quickly exhaust their energy and increase the risk of node failure. As a baseline to node failure, data packet loss also occurs in a CH due to congestion and poor link quality. Hence, one of the most crucial feature in designing a protocol is to minimize energy consumption for betterment of network functioning. Here, a clustering routing protocol based on data mining techniques is applied for sensor nodes in medical field called Adaptive Positioning of Cluster Head based Map reducing (APCH-MR) is proposed. Routing table based code book is generated for privacy concern, in which the process of mapping and reducing the data for dissemination is performed. The simulated outcomes depicts that the total number of packets transmitted in round 500 is 11200, total number of dead nodes during round 500 is 58, and time consumed by nodes at 500 rounds is 0.3751s respectively. The proposed method shows better trade off in contrast to conventional techniques.

Grouped Packet Coding: A Method for Reliable Communication Over Fading Channels With Long Delays

In this paper, we investigate an automatic repeat request (ARQ) for reliable transmission over half-duplex links. We design a method based on grouped packet coding (GPC) that combines a stop-and-wait (S&W) ARQ procedure with random linear packet coding and selective acknowledgments applied to groups of coded packets. Our goal in doing so is to boost the throughput efficiency on poor-quality links with long delay. Such links are notably encountered in underwater acoustic channels, where the bit error rate may be as high as 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> and round-trip delays can be measured in thousands of bits. To quantify the benefits of the proposed S&W-GPC method, we evaluate its throughput efficiency analytically and compare it with the throughput efficiency of standard S&W methods, as well as the benchmark efficiency of full-duplex methods. Our results show that S&W-GPC outperforms all other techniques on half-duplex links with long delay, as well as rateless packet coding on full-duplex links with long delay. We present results for a point-to-point link, as well as for a multicast network. In addition to the performance analysis, we offer guidelines for an optimal system design, which involves a judicious choice of the packet size, packet coding, and grouping parameters.

Testing Reliable-AODV for Mobile Ad-hoc Network using test-bed architecture

Mobile Ad hoc Network(MANET) provides wireless communication withoutany infrastructure. MANET generally uses a most popular and well-suited routing protocolthat is Ad hoc on Demand Distance Vector (AODV). Random topology and mobilityin MANET cause link break multiple times in network due to which poor link quality thatresults in packet losses in the network. In this paper, link quality improvement in AODV routing protocol is focused. Link quality issue can be resolved through Cross-layer design(CLD) interaction in the OSI communication model. This technique called as Reliable-AODV. CLD is implemented by interacting Physical firstlayer and network third layer of the OSI model, CLDinteraction help in strong route formation inAODV for Reliable-AODV. The result shows an improvement in the system Performance in terms of metricslike throughput, Packet Delivery Ratio(PDR) and reduction in the packet losses, delay ofthe network. Network simulator used for performance analysis is NS-2.a Simulation tool that shows improvement in results. Castadiva Emulator with Wi-Fi routers and laptop are used as test bed architecture for validating sample results of simulations. It is observed that some small variation in the simulated and emulated result by using real-time experimental setup.

Multiple Access With Polarity Division Sparse Code for Visible Light Communication

The increasing demand of mobile internet and Internet of Things poses challenging requirements for indoor wireless communication. Visible light communication (VLC) is a promising technology to alleviate the growing traffic problem. In addition, nonlinear distortion of light emitting diodes (LEDs) results in a poor link quality. In this paper, we propose multiple access with polarity division sparse code (PDSC) for VLC, which is based on nonorthogonal multiple access and spatial characteristic of LEDs. The experimental results indicate that the PDSC significantly enhances capacity and spectral efficiency of VLC network compared with orthogonal frequency division multiple access. We also demonstrate that the PDSC can optimize the problem of nonlinear distortion with polarity division. The experimental results reveal that PDSC is a promising multiple access scheme for the indoor VLC networks.

Fuzzy Trust Based Energy Aware Multipath Secure Data Collection in Wireless Sensor Network

Wireless Sensor Network (WSN) is the most promising inventions that can find its application in diverse fields such as army surveillance and forest fire detection. Multi-hop routing is followed in WSN, and the greatest security with effect to identity deception is produced through replaying routing information. An inevitable role is played by trust in the sensor network in case of military and other applications. Serious research work is being conducted on secured data aggregation. Longestablished cryptographic trust-aware routing protocols which are being used currently have become outdated, and their proficiency in tackling the situation is not much satisfactory. This ultimately results in increased complexity, poor link quality and high overhead when it comes to a number of cryptographic methods. This work deals with fuzzy logic based trust evaluation technique that can acquire secured routing. Direct Random Propagation (DRP) protocol and fuzzy logic are used in calculating the trust of the nodes. Flooding attack and black hole are proposed, and it eliminates the attack. The threshold value is compared with trust value. The ultimate result shows that the method that is proposed provides lesser interception probability, packet loss and end-to-end delay.

Channel-aware cooperative routing in underwater acoustic sensor networks

This paper proposes cooperative routing algorithms to improve the performance of underwater acoustic sensor networks (UW-ASNs). With the consideration of the poor acoustic link quality, a cross-layer design of physical layer and network layer is described for selecting simultaneously routing relays (RR) for forwarding data on routing paths and cooperative relays (CR) for one-hop cooperative communications. In the networks, sources that have data to transmit independently selects their own relays (i.e., both RR and CR) among their neighbors based on their link quality indicators (i.e., signal-to-noise ratio (SNR), time of arrival (ToA)) and their physical distances represented by hop count (HC) to the destination. Exploiting packet receiving or overhearing in the networks, these parameters are averaged and updated frequently to adapt to the unreliable and varying characteristics of acoustic channel over the time. By this way, the “best” relays with the “best” estimated measurements are selected reliably. Our simulation results show that the proposed algorithms improve the network performance in terms of end-to-end delay, energy consumption and packet delivery ratio.