Recent advances in electronics are enabling substantial processing to be performed at each node (robots, sensors) of a networked system. Local processing enables data compression and may mitigate measurement noise, but it is still slower compared to a central computer (it entails a larger computational delay). However, while nodes can process the data in parallel, the centralized computational is sequential in nature. On the other hand, if a node sends raw data to a central computer for processing, it incurs communication delay. This leads to a fundamental communication-computation trade-off, where each node has to decide on the optimal amount of preprocessing in order to maximize the network performance. We consider a network in charge of estimating the state of a dynamical system and provide three contributions. First, we provide a rigorous problem formulation for optimal real-time estimation in processing networks in the presence of delays. Second, we show that, in the case of a homogeneous network (where all sensors have the same computation) that monitors a continuous-time scalar linear system, the optimal amount of local preprocessing maximizing the network estimation performance can be computed analytically. Third, we consider the realistic case of a heterogeneous network monitoring a discrete-time multi-variate linear system and provide algorithms to decide on suitable preprocessing at each node, and to select a sensor subset when computational constraints make using all sensors suboptimal. Numerical simulations show that selecting the sensors is crucial. Moreover, we show that if the nodes apply the preprocessing policy suggested by our algorithms, they can largely improve the network estimation performance.

For almost two decades after it opened in 1913, Michigan's Central Station was a major stop on the nation's interurban rail network. Then the private car took over the US, and Detroit declined. By the 1970's, white residents were fleeing to the suburbs, auto jobs were leaving the state and the country, and local corruption soared. At the turn of the century, the train depot and the 18-story office towers behind it had been abandoned for 30 years, the faded exterior looming over Detroit's Corktown and Mexicantown neighborhoods, a sign that things were going very poorly in Detroit. By 2018, the city and Ford Motor Company were ready to tell another story.

"Alexa, which trains are going from Vienna Central Station to Linz Central Station tomorrow morning?" "Tomorrow, the train Westbahn WB 906 is departing from Vienna Central Station at 8:42 – arrival at Linz Central Station is at 9:56. Or the train Railjet RJX 262 at 8:30 from Vienna Central Station – arrival at Linz Central Station is at 9:44." Alexa, set my alarm for tomorrow at 7:30 am, and Alexa, order me an Uber for tomorrow morning at 8 am from home to Vienna Central Station." Train and taxi bookings could be done like this or similar in Austria (and all over the world) very soon. Because in a few years, voice assistants and the Internet of Voice will have changed all our lives. Although we can only try to predict what the next years are going to bring, one thing is clear: many people and companies underestimate the far-reaching changes effected by voice assistants.

An artificial intelligence (AI)-trained facial recognition system (FRS) has been installed at the Puratchi Thalaivar Dr. MGR Central railway station for detecting known culprits passing through the gates and alerting authorities. "For the first time, we have introduced the CCTV camera device backed by artificial intelligence. In the existing system, we capture the picture and video of any suspect. But we have to manually analyse the footage to detect their movement. The new system will automatically alert us about known culprits," said a senior police officer of the Government Railway Police (GRP).

Tel: 962 777 260 802 Recent advances in smart cities applications enforce security threads such as node replication attacks. Such attack is take place when the attacker plants a replicated network node within the network. Vehicular Ad hoc networks are connecting sensors that have limited resources and required the response time to be as low as possible. In this type networks, traditional detection algorithms of node replication attacks are not efficient. In this paper, we propose an initial idea to apply a newly adapted statistical methodology that can detect node replication attacks with high performance as compared to state-of-the-art techniques. We provide a sufficient description of this methodology and a road-map for testing and experiment its performance.