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Abstract This paper proposed a collaborative neurodynamic optimization (CNO) method to solve traveling salesman problem (TSP). First, we construct a Hopfield neural network (HNN) with $$n \times n$$ n × n neurons for the n cities. Second, to ensure the convergence of continuous HNN (CHNN), we reformulate TSP to satisfy the convergence condition of CHNN and solve TSP by CHNN. Finally, a population of CHNNs is used to search for local optimal solutions of TSP and the globally optimal solution is obtained using particle swarm optimization. Experimental results show the effectiveness of the CNO approach for solving TSP. 

A source submits status update jobs to a service fa- cility for processing and delivery to a monitor. The status updates belong to service classes with different service requirements. We model the service requirements using a hyperexponential service time model. To avoid class-specific bias in the service process, the system implements an M/G/1/1 blocking queue; new arrivals are discarded if the server is busy. Using an age-of-information (AoI) metric to characterize timeliness of the updates, a stochastic hybrid system (SHS) approach is employed to derive the overall average AoI and the average AoI for each service class. We observe that both the overall AoI and class-specific AoI share a common penalty that is a function of the second moment of the average service time and they differ chiefly because of their different arrival rates. We show that each high-probability service class has an associated age-optimal update arrival rate while low- probability service classes incur an average age that is always decreasing in the update arrival rate. 

In UAV communication with a ground control station, mission success requires maintaining the freshness of the received information, especially when the communication faces hostile interference. We model this problem as a game between a UAV transmitter and an adversarial interferer. We prove that in contrast with the Nash equilibrium, multiple Stackelberg equilibria could arise. This allows us to show that reducing interference activity in the Stackelberg game is achieved by higher sensitivity of the transmitter in the Stackelberg equilibrium strategy to network parameters relative to the Nash equilibrium strategy. All the strategies are derived in closed form and we establish the condition for when multiple strategies arise. 

Age of information has been proposed recently to measure information freshness, especially for a class of real-time video applications. These applications often demand timely updates with edge cloud computing to guarantee the user experience. However, the edge cloud is usually equipped with limited computation and network resources and therefore, resource contention among different video streams can contribute to making the updates stale. Aiming to minimize a penalty function of the weighted sum of the average age over multiple end users, this paper presents a greedy traffic scheduling policy for the processor to choose the next processing request with the maximum immediate penalty reduction. In this work, we formulate the service process when requests from multiple users arrive at edge cloud servers asynchronously and show that the proposed greedy scheduling algorithm is the optimal work- conserving policy for a class of age penalty functions. 

Age of information has been proposed recently to measure information freshness, especially for a class of real-time video applications. These applications often demand timely updates with edge cloud computing to guarantee the user experience. However, the edge cloud is usually equipped with limited computation and network resources and therefore, resource contention among different video streams can contribute to making the updates stale. Aiming to minimize a penalty function of the weighted sum of the average age over multiple end users, this paper presents a greedy traffic scheduling policy for the processor to choose the next processing request with the maximum immediate penalty reduction. In this work, we formulate the service process when requests from multiple users arrive at edge cloud servers asynchronously and show that the proposed greedy scheduling algorithm is the optimal work-conserving policy for a class of age penalty functions. 

We consider a real-time streaming source coding system in which an encoder observes a sequence of randomly arriving symbols from an i.i.d. source, and feeds binary code-words to a FIFO buffer that outputs one bit per time unit to a decoder. Each source symbol represents a status update by the source, and the timeliness of the system is quantified by the age of information (AoI), defined as the time difference between the present time and the generation time of the most up-to-date symbol at the output of the decoder. When the FIFO buffer is allowed to be empty, we propose an optimal prefix-free lossless coding scheme that minimizes the average peak age based on the analysis of discrete-time Geo/G/1 queue. For more practical scenarios in which a special codeword is reserved for indicating an empty buffer, we propose an encoding scheme that assigns a codeword to the empty buffer state based on an estimate of the buffer idle time. 

We consider a multicast network in which real-time status updates generated by a source are replicated and sent to multiple interested receiving nodes through independent links. The receiving nodes are divided into two groups: one priority group consists of k nodes that require the reception of every update packet, the other non-priority group consists of all other nodes without the delivery requirement. Using age of information as a freshness metric, we analyze the time-averaged age at both priority and non-priority nodes. For shifted-exponential link delay distributions, the average age at a priority node is lower than that at a non-priority node due to the delivery guarantee. However, this advantage for priority nodes disappears if the link delay is exponential distributed. Both groups of nodes have the same time-averaged age, which implies that the guaranteed delivery of updates has no effect the time-averaged freshness.