I. Ibrahim

Efficient zero-knowledge identification scheme with secret key exchange

In an open network-computing environment, a workstation cannot be trusted to identify its users correctly to network services. Identification protocols provide an approach for the receiver of a message to ascertain its origin and to verify the identity of the sender in a distributed environment. Challenge-response identification schemes have been used to provide the authentication service but it might nonetheless reveal some partial information about the claimants secret; an adversarial verifier might also be able to strategically select challenges to obtain responses providing such information. Zero-knowledge (ZK) protocols are designed to address these concerns, by allowing a prover to demonstrate knowledge of a secret while revealing no information to be used by the verifier to convey the demonstration of knowledge to others. In this paper we present a Fiat-Shamir-like zero-knowledge identification scheme based on the elliptic curve discrete logarithm problem. We combined our scheme with secret key exchange for subsequent conventional encryption. We expand our scheme to support mutual identification, for open network application

Adaptive Resource Sharing Algorithm for Device-to-Device Communications Underlaying Cellular Networks

Device-to-device (D2D) communication underlaying cellular networks allows closely located user equipments (UEs) to communicate directly by sharing the radio resources assigned to cellular UEs (CUEs). The case of multiple D2D-UEs (DUEs) sharing the same channel while each DUE can reuse multiple channels is considered. In this letter, two phases-based resource sharing algorithm is designed in such a way that its computational complexity can be adapted according to the network condition. The initial set of candidate channels that can be reused by each DUE is adaptively determined in the first phase. In the second phase, Lagrangian dual decomposition is used to determine the optimal power for DUEs that maximizing the network sum-rate. Simulation results show that the proposed algorithm can offer near-optimal performance and outperforms the comparable algorithms especially in terms of achievable throughput even with markedly reduced complexity levels.

Differentiation between Different Traffic Categories Using Multi-level of Priority in DCF-WLAN

The IEEE802.11 protocol defines access techniques to wireless medium using CSMA/CA, and defines two modes of operation in WLAN DCF and PCF. DCF does not provide any QoS, so that throughput and delay of real-time traffic, such as voice and video cannot be guaranteed over WLAN. IEEE 802.11e introduces a service differentiation mechanism based on prioritized contention access, which causes throughput degradation if the network becomes high loaded by high priority traffic. This paper proposes service differentiation for multiple access categories by using multilevel priority mechanisms in MAC layer, using multilevel priority controllability to network performance according to throughput of each traffic category.

Non-interactive Authentication Scheme Providing Privacy among Drivers in Vehicle-to-Vehicle Networks

In this paper, we present a non-interactive authentication scheme providing privacy among drivers in vehicle-to-vehicle (V2V) communication networks. Where the drivers, who are members of V2V networks, are organized into groups. Each group has a shared public key between members. Additionally, each member has a private key provided by the Third Trusted Party (TTP). In our proposed scheme, we ensure drivers privacy by allowing members to change their own set of public keys frequently using the Digital Signature Algorithm (DSA). The TTP sends to each member a token of his original set of public keys. This member can find non-interactively a new token corresponding to the new set of public keys, and hence vehicles can exchange the safety critical information without requiring a control from the TTP. In case of a malicious behavior, the identity of the signer can be revealed only by the TTP.

A Game Theoretic Framework for Device Association in Heterogeneous Cellular Networks With H2H/IoT Co-Existence

In this letter, a device association algorithm is proposed to consider the diverse association requirements for human-to-human devices (H2HDs) and Internet of Things devices (IoTDs) coexisted in heterogeneous cellular network. The association strategy was developed to jointly maximize downlink (DL) sum rate and minimize total uplink (UL) transmit power for H2HDs while maximizing UL energy efficiency for IoTDs. The association problem is formulated as a cooperative Nash bargaining game. We first develop a two-player bargaining algorithm for two base stations to bargain their associated devices. Then, based on this algorithm and the Hungarian method, a multi-player bargaining algorithm is developed. Simulation results show that the proposed algorithm outperforms the comparable schemes, especially in terms of DL rate distribution for H2HDs, device side energy efficiency for IoTDs, as well as fairness performance among devices.

TRIDNT: Isolating Dropper Nodes with Some Degree of Selfishness in MANET

In Mobile ad-hoc network, nodes must cooperate to achieve the routing purposes. Therefore, some network nodes may decide against cooperating with others; selfish nodes; to save their resources. Also these networks are extremely under threat to insider; malicious nodes; especially through packet dropping attacks.

Prediction of subcutaneous glucose concentration for type-1 diabetic patients using a feed forward neural network

Insulin Dependent Diabetes Mellitus (IDDM) is a chronic disease characterized by the inability of the pancreas to produce sufficient amount of insulin. Daily compensation of the deficiency requires 4–6 insulin injections to be taken every day. The aim of this insulin therapy is to maintain normoglycemia—i.e., a blood glucose level between 4–7 [mmol/L]. To determine the quantity and timing of these injections, several different approaches are used. Prediction of future glucose values can be used for early hypoglycemic/hyperglycemic alarms for adjustment of insulin injections or insulin infusion rates of manual or automated pumps. Recent developments in continuous glucose monitoring (CGM) devices open new opportunities for glycemia management of diabetic patients. CGM technologies provide glucose readings at high frequency and consequently detailed insight into the subjects glucose variations. The objective of this research is to use glucose readings that are obtained from CGM devices, to develop a feed forward neural network model (NNM) to predict future glucose values. This NNM can be used in model predictive control systems to automatically adjust the glucose level in type-1 diabetic patients. The results of our research indicate that the NNM can be used to accurately predict future glucose values for prediction horizons of 30 minutes or less without time delay between the predicted output and the real glucose samples.

A Recurrent Neural Network Approach for Predicting Glucose Concentration in Type-1 Diabetic Patients

Estimation of future glucose concentration is important for diabetes management. To develop a model predictive control (MPC) system that measures the glucose concentration and automatically inject the amount of insulin needed to keep the glucose level within its normal range, the accuracy of the predicted glucose level and the longer prediction time are major factors affecting the performance of the control system. The predicted glucose values can be used for early hypoglycemic/hyperglycemic alarms for adjustment of insulin injections or insulin infusion rates of manual or automated pumps. Recent developments in continuous glucose monitoring (CGM) devices open new opportunities for glycemia management of diabetic patients. In this article a new technique, which uses a recurrent neural network (RNN) and data obtained from CGM device, is proposed to predict the future values of the glucose concentration for prediction horizons (PH) of 15, 30, 45, 60 minutes. The results of the proposed technique is evaluated and compared relative to that obtained from a feed forward neural network prediction model (NNM). Our results indicate that, the RNN is better in prediction than the NNM for the relatively long prediction horizons.

Non-interactive Secure and Privacy Preserving Protocol for Inter-vehicle Communication Networks

In this paper, we introduce a non-interactive secure protocol preserving privacy of the drivers for Inter-Vehicle Communication (IVC) networks. To protect the privacy among drivers, we propose to arrange vehicles into several groups. Vehicles in a group share the same public key, but each member can change his own set of public keys frequently, so the receiving vehicle cannot identify an individual driver in the group. In addition, each member has a private key provided by the Third Trusted Party (TTP) to enable the TTP, who is assumed to be fully trusted, to trace the driver who sends malicious information. Then, the TTP computes a fixed token of all members in the same group, but only participants in IVC networks can convince the receiving vehicle that the token is corresponding to their changed public keys set. So, we can achieve authentication.

Joint channel selection and optimal power allocation for multi-cell D2D communications underlaying cellular networks

Device-to-device (D2D) communication underlaying cellular networks can improve the spectrum efficiency as a result of sharing the radio resources allocated to cellular user equipments (CUEs). However, the severe interference between D2D and CUEs communications may lead to performance degradation of cellular system if not coordinated properly. In this study, a joint channel assignment and power allocation algorithm is proposed, which addresses the intra-cell and inter-cell interference management problems for D2D communication underlaying cellular network. The case of multiple D2D-user equipments (DUEs) sharing the same channel while each DUE can reuse multiple channels is considered. The proposed algorithm is designed with two complementary steps in such a way that its computational complexity can be adapted according to the network condition. The preliminary set of CUEs candidate channels that can be reused by each DUE is adaptively decided in the first step. In the second step, the optimal power allocation for each DUE is determined using Lagrangian dual decomposition to maximise the network sum-rate. Simulation results show that the proposed algorithm outperforms the current comparable algorithms especially in terms of achievable throughput. Moreover, the effect of various system parameters on the performance of the proposed technique is also investigated.