## 1 Introduction  
The number of embedded devices has continuously increased in recent years. Traditionally, such devices have worked locally in an independent way and provided services for human users. Advances in radio communication technologies have enabled even mobile connectivity for the referred devices over the Internet. These trends are now visible as the increasing number of application cases which rely on the services exposed from physical equipment, such as sensors, actuators, RFID tags, machines, vehicles and industrial embedded devices. Such service systems are described here as Machine to Machine (M2M) service networks, and they can also be called Internet of Things (IoT), Web of Things, Wireless Sensor Networks and Actuator Networks (WSANs) or Cyber Physical Systems [1; 2]. Usually, such M2M service networks include capabilities for remote measurements and remote control of embedded devices. Remote measurements consist of sensing physical phenomenon, storing, sending, receiving and processing of measured information. Remote control of devices includes access control, mutual exclusion, sending, receiving and processing of control commands. The basic enabler for such functionality is M2M connectivity, which links various kinds of embedded devices into the Internet based M2M services. The added value is created by the enabled M2M services based on the use of the measured information in a smart way, reasoning, and execution of smart remote control actions with the M2M asset devices. The real benefits of such smart M2M services will be realized when connecting them with the core processes of the companies. This can enable real-time situation awareness in the company processes, and create opportunities for novel services for customers, increase service quality and enable cost savings, e.g., in maintenance processes. Furthermore, this is also an enabler for transition from product centric to service centric businesses. The main problems in these M2M service networks arise from the complexity and the vertical fragmentation of M2M markets. This complexity is due to the number of embedded devices, connectivity means, and service platforms and especially to their heterogeneity. Establishing and maintaining an interactive system capable of interoperating with the human user is here expected to go beyond human capabilities soon. M2M market is fragmented to multiple vertical industries, and the resulting systems are usually domain or vendor specific closed systems, also called \"vertical silos\". In addition, the natural needs of businesses to protect themselves seem to lead such systems that require special access rights for each specific system, resulting in vendor specific closed systems. This has caused problems, for example in residential home environments, and prevented the emergence of home automation to a large extent. Smart grid solutions cannot interoperate with infrastructure and buildings/homes, even if it would be strongly required to reach higher level energy efficiency. Therefore, it is observed here that the technological complexity and vertical M2M silos are the cause of a _grand research challenge_ for the development of a modern ecosystem. It is assumed here that the referred challenge is so fundamental that it requires novel approaches for the system architectures and application of novel paradigms. It is seen that Internet \"IP everywhere\" cannot alone solve the problem with industrial M2M systems. Something more is needed, such as horizontal approach and autonomic computing, which may have potential to create basis for solving this sizable challenge. Most of the existing vertical M2M solutions have difficulty in scaling, and therefore enabling the _horizontal model_ is important for realizing embedded M2M [3]. Autonomic computing is a concept inspired from biological systems that aims to develop systems capable of automatic management for solving the complexity problem [4,5].